Georgiadis, Kostas; Kalaganis, Fotis P; Oikonomou, Vangelis P; Nikolopoulos, Spiros; Laskaris, Nikos A; Kompatsiaris, Ioannis
International Conference on Brain Informatics, Springer 2023.
Neuromarketing exploits neuroimaging techniques to study consumers’ responses to various marketing aspects, with the goal of gaining a more thorough understanding of the decision-making process. The neuroimaging technology encountered the most in neuromarketing studies is Electroencephalography (EEG), mainly due to its non-invasiveness, low cost and portability. Opposed to typical neuromarketing practices, which rely on signal-power related features, we introduce an efficient decoding scheme that is based on the principles of Riemannian Geometry and realized by means of a suitable deep learning (DL) architecture (i.e., SPDNet). We take advantage of a recently released, multi-subject, neuromarketing dataset to train SPDNet under the close-to-real-life scenario of product selection from a supermarket leaflet and compare its performance against standard tools in EEG-based neuromarketing. The sample covariance is used as an estimator of the ‘quasi-instantaneous’, brain activation pattern and derived from the multichannel signal recorded while the subject is gazing at a given product. Pattern derivation is followed by proper re-alignment to reduce covariate shift (inter-subject variability) before SPDNet casts its binary decision (i.e., “Buy”-“NoBuy”). The proposed decoder is characterized by sufficient generalizability to derive valid predictions upon unseen brain signals. Overall, our experimental results provide clear evidence about the superiority of the DL-decoder relatively to both conventional neuromarketing and alternative Riemannian Geometry-based approaches, and further demonstrate how neuromarketing can benefit from recent advances in data-centric machine learning and the availability of relevant experimental datasets.
Mizrahi, Dor; Laufer, Ilan; Zuckerman, Inon
International Conference on Brain Informatics, Springer 2023.
Tags: DSI-24| |
Attachment theory is concerned with the basic level of social connection associated with approach and withdrawal mechanisms. Consistent patterns of attachment may be divided into two major categories: secure and insecure. As secure and insecure attachment style individuals vary in terms of their responses to affective stimuli and negatively valanced cues, the goal of this study was to examine whether there are differences in Beta power activation between secure and insecure individuals to feedback given while performing the arrow flanker task. An interaction emerged between Attachment style (secure or insecure) and Feedback type (success or failure) has shown differences in Beta power as a function of both independent factors. These results corroborate previous findings indicating that secure and insecure individuals differently process affective stimuli.
Chiossi, Francesco; Turgut, Yagiz; Welsch, Robin; Mayer, Sven
In: Proc. ACM Hum.-Comput. Interact, vol. 7, 2023.
Biocybernetic loops encompass users’ state detection and system adaptation based on physiological signals. Current adaptive systems limit the adaptation to task features such as task difficulty or multitasking demands. However, virtual reality allows the manipulation of task-irrelevant elements in the environment. We present a physiologically adaptive system that adjusts the virtual environment based on physiological arousal, i.e., electrodermal activity. We conducted a user study with our adaptive system in social virtual reality to verify improved performance. Here, participants completed an n back task, and we adapted the visual complexity of the environment by changing the number of non-player characters. Our results show that an adaptive virtual reality can control users’ comfort, performance, and workload by adapting the visual complexity based on physiological arousal. Thus, our physiologically adaptive system improves task performance and perceived workload. Finally, we embed our findings in physiological computing and discuss applications in various scenarios
Georgiadis, Kostas; Kalaganis, Fotis P; Riskos, Kyriakos; Matta, Eleftheria; Oikonomou, Vangelis P; Yfantidou, Ioanna; Chantziaras, Dimitris; Pantouvakis, Kyriakos; Nikolopoulos, Spiros; Laskaris, Nikos A; others,
In: Scientific Data, vol. 10, no. 1, pp. 508, 2023.
Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers’ behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing.
Riek, Nathan T; Susam, Busra T; Hudac, Caitlin M; Conner, Caitlin M; Akcakaya, Murat; Yun, Jane; White, Susan W; Mazefsky, Carla A; Gable, Philip A
In: Journal of Autism and Developmental Disorders, pp. 1–11, 2023.
The purpose of this study is to investigate if feedback related negativity (FRN) can capture instantaneous elevated emotional reactivity in autistic adolescents. A measurement of elevated reactivity could allow clinicians to better support autistic individuals without the need for self-reporting or verbal conveyance. The study investigated reactivity in 46 autistic adolescents (ages 12–21 years) completing the Affective Posner Task which utilizes deceptive feedback to elicit distress presented as frustration. The FRN event-related potential (ERP) served as an instantaneous quantitative neural measurement of emotional reactivity. We compared deceptive and distressing feedback to both truthful but distressing feedback and truthful and non-distressing feedback using the FRN, response times in the successive trial, and Emotion Dysregulation Inventory (EDI) reactivity scores. Results revealed that FRN values were most negative to deceptive feedback as compared to truthful non-distressing feedback. Furthermore, distressing feedback led to faster response times in the successive trial on average. Lastly, participants with higher EDI reactivity scores had more negative FRN values for non-distressing truthful feedback compared to participants with lower reactivity scores. The FRN amplitude showed changes based on both frustration and reactivity. The findings of this investigation support using the FRN to better understand emotion regulation processes for autistic adolescents in future work. Furthermore, the change in FRN based on reactivity suggests the possible need to subgroup autistic adolescents based on reactivity and adjust interventions accordingly.
Swerdloff, Margaret M; Hargrove, Levi J
Tags: DSI-7| |
The cognitive load of a precisely timed task, such as the Stroop task, may be measured through the use of eventrelated potentials (ERPs). To determine the time at which cognitive load is at its peak, oddball tones may be applied at various times surrounding a cognitive task. However, we need to determine whether the simultaneous presentation of auditory and visual stimuli would mask a potential change in P3 in an ERP-producing task. If the contribution of the Stroop stimulus is too large, then Stroop ERP with oddball stimuli occurring at different timepoints may not be directly comparable across the various timepoints due to the contribution of the Stroop ERP. The aim of this study was to measure the magnitude of the difference wave between that of simultaneously presented stimuli and that of linearly added stimuli of separate responses. Participants were fitted with a dry-sensor EEG cap and were presented with a series of Stroop and auditory stimuli. For some Stroop stimuli, auditory stimuli occurred simultaneously or in a close time proximity to the Stroop stimuli. We sought to estimate the linear contribution of the ERP from Stroop and oddball stimuli. We found that the magnitude of the difference waves were 3.07 ± 1.65 µV and 2.82 ± 1.34 µV for congruent and incongruent stimuli, respectively. As the average amplitude in the P3 region for both the congruent and incongruent difference waves was lower than the magnitude of the auditory oddball presented simultaneously with Stroop stimuli (12.13 ± 1.00 µV for congruent and 11.78 ± 1.05 µV for incongruent Stroop), we expect that the contribution of P3 auditory oddball would not mask a potential Stroop effect even if the timing of the auditory oddball stimuli were experimentally manipulated, a direction that we hope to explore in future work. In conclusion, we determine this paradigm is suitable for measuring cognitive load in precisely timed tasks.
Kim, Suhye; Kim, Jung-Hwan; Hyung, Wooseok; Shin, Suhkyung; Choi, Myoung Jin; Kim, Dong Hwan; Im, Chang-Hwan
In: IEEE Transactions on Learning Technologies, 2023.
With the widespread application of online education platforms, the necessity for identifying learner's mental states from webcam videos is increasing as it can be potentially applied to artificial intelligence-based automatic identification of learner's states. However, the behaviors that elementary school students frequently exhibit during online learning particularly when they are in a low attention state have rarely been investigated. This study employed electroencephalography (EEG) to continuously track changes in the learner's attention state during online learning. A new EEG index reflecting elementary students' attention level was developed using an EEG dataset acquired from 30 fourth graders during a computerized d2 test of attention. Characteristic behaviors of 24 elementary students in a low attention state were then identified from the webcam videos showing their upper bodies captured during 40-minute online lectures, with the proposed EEG index being used as a reference to determine their attention level at the time. Various characteristic behaviors were identified regarding participant's mouth, head, arms, and torso. For example, opening mouth or leaning back was observed more frequently in a low attention state than in a high attention state. It is expected that the characteristic behaviors reflecting learner's low attention state would be utilized as a useful reference in developing more interactive and effective online education systems.
Cho, Ji Young; Wang, Ze-Yu; Hong, Yi-Kyung
In: Journal of the Korean Housing Association, vol. 34, no. 3, pp. 067–079, 2023.
Tags: DSI-24| |
This study aimed to identify the attributes of environmental design elements that promote healing. Eighteen college students participated in an EEG study, and their Ratio of Alpha to Beta (RAB) was examined in relation to eight interior design stimuli consisting of different conditions of illuminance (high/low), color (green/white), and biophilia ( natural/artificial). The EEGs of the 19 channels were measured using a dry-type EEG device (DSI-24). Participants also completed a survey questionnaire, choosing the best stimuli for healing environments and for wanting to stay. The EEG data were analyzed using a Wilcoxon signed-rank test to identify statistical differences in responses to different stimuli. The main results were as follows: (1) RAB was most frequently observed in the left temporal and parietal lobes; (2) when comparing pre- and post-stimulation, significant differences were observed between background EEG and six stimuli (S1, S3, S5, S6, S7, and S8); (3) RAB tended to be high with low illuminance, white walls, and artificial biophilia; and (4) the psychological and EEG responses did not match; stimuli with high illuminance and natural biophilia were selected the most for healing and wanting to stay environments. The results indicate the necessity for studying environmental responses via both physiological and psychological aspects as they compensate for each other. and artificial biophilia; and (4) the psychological and EEG responses did not match; stimuli with high illuminance and natural biophilia were selected the most for healing and wanting to stay environments. The results indicate the necessity for studying environmental responses via both physiological and psychological aspects as they compensate for each other. and artificial biophilia; and (4) the psychological and EEG responses did not match; stimuli with high illuminance and natural biophilia were selected the most for healing and wanting to stay environments. The results indicate the necessity for studying environmental responses via both physiological and psychological aspects as they compensate for each other.
University of Washington, 2023.
Tags: DSI-7| |
Classification of motor tasks is of significant interest in brain-computer interfacing today. Electroencephalograph data contains a large amount of noise obfuscating the signal associated with these motor tasks. Various preprocessing techniques exist to increase the signalto-noise ratio allowing for more accurate classifications. The effectiveness of these techniques varies between motor tasks and in different environments. There is a need to evaluate these different techniques in many different environments and with different motor tasks. This thesis investigates the effectiveness of several preprocessing techniques and classification models for classifying four different motor imagery tasks from EEG data. Specifically, Frequency Filtering, ICA, and CSP are evaluated using Naive Bayes, kNN, Linear SVM, RBF SVM, LDA, Random Forest, and a MLP Neural Network. To control for the environment, data was collected from student volunteers in short sessions designed to demonstrate either eye blinking, eye rolling, jaw clenching, or neck turning. Each task had its own procedure for the session. Motor tasks in data were evaluated for frequency and amplitude commonalities using continuous wavelet transforms and Fourier transforms. Preprocessing Techniques were then iteratively applied to these datasets and evaluated using an ML model. The evaluation metrics used were Accuracy, F1, Precision, and Recall. Results showed that the combination of Frequency Filtering, ICA, and CSP with the Naive Bayes and Random Forest models yielded the highest accuracy and F1 for all motor tasks. These findings contribute to the field of EEG signal processing and could have potential applications in the development of brain-computer interfaces. It also directly contributes to a greater project in spatial neglect rehabilitation by providing novel insights to common artifacts in EEG data, as well as to the creation of a framework for data processing in real-time and offline.
Fan, Tengwen; Zhang, Liming; Liu, Jianyi; Niu, Yanbin; Hong, Tian; Zhang, Wenfang; Shu, Hua; Zhao, Jingjing
In: Neuropsychologia, pp. 108624, 2023.
Tags: DSI-24| |
Poor phonological awareness is associated with greater risk for reading disability. The underlying neural mechanism of such association may lie in the brain processing of phonological information. Lower amplitude of auditory mismatch negativity (MMN) has been associated with poor phonological awareness and with the presence of reading disability. The current study recorded auditory MMN to phoneme and lexical tone contrast with odd-ball paradigm and examined whether auditory MMN mediated the associations between phonological awareness and character reading ability through a three-year longitudinal study in 78 native Mandarin-speaking kindergarten children. Hierarchical linear regression and mediation analysis showed that the effect of phoneme awareness on the character reading ability was mediated by the phonemic MMN in young Chinese children. Findings underscore the key role of phonemic MMN for the underlying neurodevelopmental mechanism linking phoneme awareness and reading ability.
Yu, Heeseung; Han, Eunkyoung
People see what they want to see: an EEG study Journal Article
In: Cognitive Neurodynamics, pp. 1–15, 2023.
Tags: DSI-24| |
This study explored selective exposure and confirmation bias in the choices participants made about which political videos to watch, and whether their political positions changed after they watched videos that either agreed with or opposed their positions on two controversial issues in South Korea: North Korea policy and social welfare policy. The participants completed questionnaires before and after they watched the videos, were asked to select thumbnails of videos before they watched any, and had their brain wave activity measured through electroencephalogram (EEG) as they watched both types of videos. The participants demonstrated selective exposure as they primarily selected video thumbnails with content that matched their political orientations, and they demonstrated confirmation bias as their questionnaire responses after they watched the videos indicated that their positions had hardened. There were also statistically significant differences in alpha, beta, sensory motor rhythm, low beta, mid beta, and fast alpha activity depending on the political orientation consistency between the participants and the videos. Future studies could expand this line of research beyond college students and beyond Asia, and longitudinal work could also be conducted to determine if the obtained patterns remain constant over time.
Demarest, Phillip; Rustamov, Nabi; Swift, James; Xie, Tao; Adamek, Markus; Cho, Hohyun; Wilson, Elizabeth; Han, Zhuangyu; Belsten, Alexander; Luczak, Nicholas; others,
Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4–7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity and pain interference scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.
Ferrisi, Leonardo M
Brain Computer Interfaces (BCIs) allow individuals to operate technology using (typically consciously controllable) aspects of their brain activity. Auditory BCIs utilize principles of Auditory Event Related
Potentials or Auditory Evoked Potentials as a reproducible controllable features that individuals can use to operate a BCI. These Auditory BCIs in their most basic format can allow users to answer yes or no questions by listening to either one auditory stimuli or the other. Current accuracy in intended response detection for these kinds of BCIs can be as good as mean accuracy of 77 % . BCI research tends to optimize the computer side of the system however the ease of use for the human operating the system is an important point of consideration as well. This research project aimed to determine what factors make a human operator able to achieve the highest accuracy using a given previously successfully demonstrated classifier. This research project primarily sought to answer the questions; to what degree people can improve their accuracy in operating an Auditory BCI and what factors of the stimulus used can be altered to achieve this. The results of this project, obtained through the data collected from six individuals, found that slower stimuli speeds for eliciting Auditory Event Related Potentials were significantly better at achieving higher prediction accuracies compared to faster stimulus speeds. The amount of time spent using the system appeared to result in diminishing returns in accuracy regardless of condition however not before an initial spike in greater classifier prediction accuracy for the second condition run on each subject. Although further research is needed to gain more conclusive evidence for or against the hypothesis, the results of this research may be able suggest that individuals can improve their performance using Auditory BCIs with practice at optimal parameters albeit within a given time frame before experiencing diminishing returns. These findings would stand to provide benefit both to continued research in making optimal non-invasive alternative communication technologies as well as making progress in finding the potential ceiling in accuracy that an Auditory BCI can have in interpreting brain activity for the intended action of the user
Maffei, Luigi; Masullo, Massimiliano
In: Institute of Noise Control Engineering, vol. 266, no. 2, pp. 134–140, 2023.
Tags: DSI-24| |
Recent advances in developing new tools and miniaturised devices to measure, analyse, model and simulate existing or future projects are more and more influencing the way to investigate and solve problems of various disciplines fostering deep changes in the research paradigms toward human-centred, multisensory and multidisciplinary approaches. In acoustics, beyond the negative effect of noise on individuals and its mitigation, researchers are even more interested in investigating how the complexity of the multisensory environment modulates the individuals' holistic experience. To this aim, the Department of the Università degli Studi della Campania "Luigi Vanvitelli" has built the Sens i-Lab, a key facility integrating, in a single test room, the simulation and control of the physical environment (acoustics, vision, lighting, microclimate, IAQ) with advanced systems for simulation of virtual environments. To complement the simulation and control of the stimuli, the Sens i-Lab is equipped with a set of systems and devices for motion tracking, for the measurement of the biofeedback signals (EEG, EDA, HRV, VAF) and their association with environmental stimuli and self-reported psychological measures of people well-being. Taking advantage of the Sens i-Lab setting, new research fields and applications in acoustics are possible. Some of them are presented.
Chiossi, Francesco; Ou, Changkun; Mayer, Sven
Physiologically-adaptive Virtual Reality can drive interactions and adjust virtual content to better fit users’ needs and support specific goals. However, the complexity of psychophysiological inference hinders efficient adaptation as the relationship between cognitive and physiological features rarely show one-to-one correspondence. Therefore, it is necessary to employ multimodal approaches to evaluate the effect of adaptations. In this work, we analyzed a multimodal dataset (EEG, ECG, and EDA) acquired during interaction with a VR-adaptive system that employed EDA as input for adaptation of secondary task difficulty. We evaluated the effect of dynamic adjustments on different physiological features and their correlation. Our results show that when the adaptive system increased the secondary task difficulty, theta, beta, and phasic EDA features increased. Moreover, we found a high correlation between theta, alpha, and beta oscillations during difficulty adjustments. Our results show how specific EEG and EDA features can be employed for evaluating VR adaptive systems.
Lim, Hyunmi; Jeong, Chang Hyeon; Kang, Youn Joo; Ku, Jeonghun
In: Cyberpsychology, Behavior, and Social Networking, 2023.
Brain–computer interface (BCI) is a promising technique that enables patients' interaction with computers or machines by analyzing specific brain signal patterns and provides patients with brain state-dependent feedback to assist in their rehabilitation. Action observation (AO) and peripheral electrical stimulation (PES) are conventional methods used to enhance rehabilitation outcomes by promoting neural plasticity. In this study, we assessed the effects of attentional state-dependent feedback in the combined application of BCI-AO with PES on sensorimotor cortical activation in patients after stroke. Our approach involved showing the participants a video with repetitive grasping actions under four different tasks. A mu band suppression (8–13 Hz) corresponding to each task was computed. A topographical representation showed that mu suppression of the dominant (healthy) and affected hemispheres (stroke) gradually became prominent during the tasks. There were significant differences in mu suppression in the affected motor and frontal cortices of the stroke patients. The involvement of both frontal and motor cortices became prominent in the BCI-AO+triggered PES task, in which feedback was given to the patients according to their attentive watching. Our findings suggest that synchronous stimulation according to patient attention is important for neurorehabilitation of stroke patients, which can be achieved with the combination of BCI-AO feedback with PES. BCI-AO feedback combined with PES could be effective in facilitating sensorimotor cortical activation in the affected hemispheres of stroke patients.
Seo, Seoung Won; Kim, Yong Seong
In: The Journal of Korean Physical Therapy, vol. 35, no. 1, pp. 19–23, 2023.
Purpose: The purpose of this study is to analyze the brain waves and develop various exercise programs to improve the physical and mental aspects of stroke patients when neurological physical therapy and sitting table tennis exercise are applied to stroke patients.
Methods: In this study, an experiment was conducted on 15 patients diagnosed with stroke, and training was performed after changing the ping-pong table to a sitting position to apply ping-pong exercise to stroke patients. After training was conducted for 40 minutes twice a week for 4 weeks, brain waves were measured before and after. EEG was measured using Laxtha’s DSI-24 equipment as a measurement tool, and data values were extracted through the Telescan program.
Results: Most of the relative beta waves showed a significant difference before and after the intervention. As for the characteristics of beta waves, this result can be seen as being highly activated during exercise or other activities.
Conclusion: Ping-pong exercise in a sitting position is a good intervention method for stroke patients, and it can help to use it as basic data in clinical practice by showing brain activity.
Chen, Sheng; Xie, Haiqun; Yang, Hongjun; Fan, Chenchen; Hou, Zengguang; Zhang, Chutian
In: Intelligent Robotics: Third China Annual Conference, CCF CIRAC 2022, pp. 349–361, 2023.
Mild cognitive impairment (MCI) is the preliminary stage of dementia, and has a high risk of progression to Alzheimer’s disease (AD) in the elderly. Early detection of MCI plays a vital role in preventing progression of AD. Clinical diagnosis of MCI requires many examinations, which are highly demanding on hospital equipment and expensive for patients. Electroencephalography (EEG) offers a non-invasive and less expensive way to diagnose MCI early. In this paper, we propose a multi-modal fusion classification framework for MCI detection. We collect EEG data using a delayed match-to-sample task and analyze the differences between the two groups. Based on analysis results, we extract Power spectral density (PSD), PSD enhanced, Event-related potential (ERP) features in EEG signal along with physiological features and behavioral features of the subjects to classify MCI and healthy elderly. By comparing the effect of different features on classification performance, we find that the time-domain based ERP features are better than the frequency-domain based PSD or PSD enhanced features to overcome inter-individual differences to distinguish MCI, and these two features have good complementarity, fusing ERP and PSD enhanced features can greatly improve the classification accuracy to 84.74%. The final result shows that MCI and healthy elderly can be well classified by using this framework.
Kambhamettu, Sudhendra; Cruz, Meenalosini Vimal; Anitha, S; Chakkaravarthy, S Sibi; Kumar, K Nandeesh
In: Brain-Computer Interface: Using Deep Learning Applications, pp. 205–229, 2023.
Technology today serves millions of people suffering from mobility impairments across the globe in numerous ways. Although advancements in medicine and healthcare systems improve the life expectancy of the general population, sophisticated engineering techniques and computing processes have long facilitated the patient in the recovery process. People struggling with mobility impairments and especially spine injuries which also leads to loss of speech, often have a narrow group of devices to aid them move from place-to-place and they are often limited to just movement functionality. BCI (Brain Computer Interface) powered wheelchairs leverage the power of the brain, i.e. translating the thoughts/neural activity into real-world movement providing automated motion without any third party intervention. Many BCI powered wheelchairs in the market are cumbersome to operate and provide only singular functionality of movement. To address this problem and improve the state of BCI products, Cerebro introduces the first ever go-to market product utilizing Artificial Intelligence to facilitate mobility features with built-in speech functionality via blink detection. Further sections of the Chapter take an in-depth look into each layer of the Cerebro system.
Dhaliwal, BS; Haddad, J; Debrincat, M; others,
Changes in Electroencephalogram (EEG) After Foot Stimulation with Embedded Haptic Vibrotactile Trigger Technology: Neuromatrix and Pain Modulation Considerations. Anesth Pain Res. 2022; 6 (2): 1-11 Journal Article
In: Correspondence: Peter Hurwitz, Clarity Science LLC, vol. 750, 2022.
Background: Globally, pain and pain-related diseases are the leading causes of disability and disease burden. In the United States, pain is the most common reason patients consult primary care providers. An estimated 100 million people live with chronic or recurrent pain. Existing pharmacological treatments for pain include anti-inflammatory agents, opioids, and other oral and topical analgesics. Many of these have been associated with troublesome and potentially harmful adverse effects. Understanding the complex pain neuromatrix may help in identifying alternative, non-invasive strategies and treatment approaches to address pain severity, interference, and improve patient outcomes. The neuromatrix of pain is a network of neuronal pathways and circuits responding to sensory (nociceptive) stimulation. Research has suggested that the output patterns of the body-self neuromatrix are responsible for causing or triggering perceptual, homeostatic, and behavioral programs following traumatic injury, other pathology, or chronic stress. As such, pain can be considered a product of the output of a widely distributed neural network within the brain instead of a sequential result of sensory inputs triggered by injury, inflammation, or other pathology. For over a century, the Brodmann Areas remain the most widely known and frequently cited cytoarchitectural organization of the human cortex. Certain Brodmann areas of the brain have been associated with the current understanding of the neuromatrix of pain. The areas expands well beyond the thalamus and anterior cingulate, and primary (S1) and secondary (S2) somatosensory cortices to include the midbrain region of the periaqueductal gray (PAG) and the lenticular complex as well as the insula, orbitofrontal (Brodmann's area [BA] 11, 47), prefrontal (BA 9, 10, 44-46), motor (BA 6, Supplementary motor area, and M1), inferior parietal (BA 39, 40), and anterior cingulate (BA 24, 25) cortices (ACCs). Treatments that are non-invasive and non-pharmacological and target both central and peripheral nociceptive mechanisms that are identified as having an impact on the Brodmann areas associated with the neuromatrix of pain may potentially be considered a beneficial pain management option for patients. Haptic vibrotactile trigger technology targets the nociceptive pathways and is theorized to disrupt the neuromatrix of pain. The technology has been incorporated into non-pharmacological patches and other non-invasive routes of delivery such as apparel (socks), braces, wristbands, and compression sleeves. The purpose of this minimal risk study was to compare electroencephalogram (EEG) patterns in areas of the brain that have been associated with the neuromatrix for pain in subjects wearing socks that were embedded with haptic vibrotactile trigger technology with those patients that wore socks that were not embedded with the technology.
Methods: This IRB-approved study compared electroencephalogram (EEG) patterns in subjects wearing cloth socks embedded with haptic vibrotactile trigger technology (Superneuro VTT Enhanced Socks (Srysty Holding Co., Toronto, Canada) with those patients that wore cloth socks that were not embedded with the technology. Baseline EEG data from 19 scalp locations were recorded in sixty (60) adult subjects (36 females and 24 males) ranging from ages 14 to 83 wearing standard store-purchased cloth socks on their feet. The subject’s standard socks were then removed and replaced with the Superneuro VTT enhanced socks on the subject’s feet. A second EEG recording was then obtained. Both eyes-closed and eyes-open data were recorded.
Results: The results showed statistically significant t-test differences (P < .01) in 59 out of 60 subjects in absolute power and 60 out of 60 subjects showed statistically significant differences in coherence and phase difference. The largest differences were in the alpha1 and beta2 frequency bands and especially in central scalp locations. Paired t-tests of LORETA current source densities between socks on and socks off demonstrated statistically significant differences in 60 out of 60 subjects. The largest effects of Superneuro VTT enhanced socks on were on the medial bank of the somatosensory cortex as well as in the left frontal lobes in the theta and alpha frequency.
Conclusions: Study results indicate that foot stimulation with embedded haptic vibrotactile trigger technology showed significant modulation in the Brodmann areas that have been shown to be associated with the neuromatrix for pain in the human brain. Further research is suggested to evaluate if this technology has a positive impact on pain severity, pain interference, and quality of life and to be considered as a potentially beneficial pain management strategy and as part of a multi-modal treatment approach.
Ocay, Don Daniel; Teel, Elizabeth F; Luo, Owen D; Savignac, Chloé; Mahdid, Yacine; Blain-Moraes, Stefanie; Ferland, Catherine E
In: PAIN Reports, vol. 7, no. 6, pp. e1054, 2022.
The pathophysiology of pediatric musculoskeletal (MSK) pain is unclear, contributing to persistent challenges to its management.
This study hypothesizes that children and adolescents with chronic MSK pain (CPs) will show differences in electroencephalography (EEG) features at rest and during thermal pain modalities when compared with age-matched controls.
One hundred forty-two CP patients and 45 age-matched healthy controls (HCs) underwent a standardized thermal tonic heat and cold stimulations, while a 21-electrode headset collected EEG data. Cohorts were compared with respect to their EEG features of spectral power, peak frequency, permutation entropy, weight phase-lag index, directed phase-lag index, and node degree at 4 frequency bands, namely, delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz), at rest and during the thermal conditions.
At rest, CPs showed increased global delta (P = 0.0493) and beta (P = 0.0002) power in comparison with HCs. These findings provide further impetus for the investigation and prevention of long-lasting developmental sequalae of early life chronic pain processes. Although no cohort differences in pain intensity scores were found during the thermal pain modalities, CPs and HCs showed significant difference in changes in EEG spectral power, peak frequency, permutation entropy, and network functional connectivity at specific frequency bands (P < 0.05) during the tonic heat and cold stimulations.
This suggests that EEG can characterize subtle differences in heat and cold pain sensitivity in CPs. The complementation of EEG and evoked pain in the clinical assessment of pediatric chronic MSK pain can better detect underlying pain mechanisms and changes in pain sensitivity.
Chang, Won Kee; Lim, Hyunmi; Park, Seo Hyun; Lim, Chaiyoung; Paik, Nam-Jong; Kim, Won-Seok; Ku, Jeonghun
Background: This study aimed to investigate the activation pattern of the motor cortex (M1) and parietal cortex during immersive virtual reality (VR)-based mirror visual feedback (MVF) of the upper limb in patients with chronic stroke. Methods: Fourteen patients with chronic stroke with severe upper limb hemiparesis (Brunnstrom stage of hand 1-3) and 21 healthy controls were included. The participants performed wrist extension tasks with their unaffected wrists (or the dominant side in controls). In the MVF condition, the movement of the affected hand was synchronized with that of the unaffected hand. In contrast, only the movement of the unaffected hand was shown in the no-MVF condition. Electroencephalography was obtained during experiments with two conditions (MVF vs no-MVF). Mu suppression in the bilateral M1 and parietal cortex and mu coherence between the ipsilateral M1 and parietal cortex in each hemisphere and interhemispheric M1 were used for analyses. Results: In patients with stroke, MVF induced significant mu suppression in both the ipsilesional M1 and parietal lobes (p=0.006 and p=0.009, respectively), while significant mu suppression was observed in the bilateral M1 (p=0.003 for ipsilesional and p=0.041 for contralesional M1, respectively) and contralesional (contralateral hemisphere to the moving hand) parietal lobes in the healthy controls (p=0.036). The ipsilesional mu coherence between the M1 and parietal cortex in patients with stroke was stronger than that in controls regardless of MVF condition (p<0.001), while mu coherence between interhemispheric M1 cortices was significantly weaker in patients with stroke (p=0.032). Conclusion: In patients with stroke, MVF using immersive VR induces mu suppression in the ipsilesional M1 and parietal lobe. Our findings provide evidence of the neural mechanism of MVF using immersive VR and support its application in patients with stroke with severe hemiparesis.
Chanpornpakdi, Ingon; Noda, Motoi; Tanaka, Toshihisa; Harpaz, Yuval; Geva, Amir B
Proceedings of 2022 APSIPA Annual Summit and Conference, 2022.
Packaging and advertisements of brands affect customers’ decision-making on purchasing products and could lead to business loss. Hence, neuromarketing, the application of neuroscience in the marketing field, is introduced aiming to understand customers’ cognitive functions toward advertisements or products. Our study focused on identifying how the brain respond to different types of advertising image of the same brand were perceived using electroencephalogram (EEG). We performed an experiment using 33 different Coca-Cola advertising images in RSVP (rapid serial visual presentation) task on 23 participants. A seven channels EEG dry headset was used to record the visual event-related potential (ERP), specifically, the positive peak found at 300 to 700 ms after image onset; P300, to compare the perception response. We applied k-means and hierarchical clustering to the obtained EEG data, and achieved the best clustering for three clusters, yielding different P300 amplitudes and latencies. The typical Coca-Cola ads, red color with Cola-cola text on the ads, induced a faster and larger response, implying better perception than the unconventional or black color ads. We conclude that ERP clustering may be a useful tool for neuromarketing. However, the relationship between the EEG-based cluster and the image-based cluster should be further investigated to confirm the suggestion.
Woo, Hee-Soon; Song, Chiang-Soon
In: Physical Therapy Rehabilitation Science, vol. 11, no. 3, pp. 296–303, 2022.
Tags: DSI-24| |
Objective: In general, macular degeneration, cataracts and glaucoma generally cause visual injury in clinical settings. This study aimed to examine the effects of low visual acuity simulations on hand manual dexterity function and brainwaves in healthy young adults.
Design: Cross-sectional study design
Methods: This study was an observational, cross-sectional study. Seventy healthy young adults participated in this study. To evaluate the effects of low visual acuity simulations on hand function and brain waves, this study involved four different visual conditions including (1) normal vision, (2) simulated cataracts, (3) simulated glaucoma, and (4) simulated macular degeneration. The hand function was measured to use the Minnesota manual dexterity test (MMDT), and the brainwaves was also measured to use the electroencephalography.
Results: In hand function, placing and turning performance on the MMDT in the normal visual condition was significantly different than that in the cataract and macular degeneration conditions (p<0.05), and the placing performance was significantly differred in the normal condition than that in the simulated glaucoma. However, turning was not significantly different in the normal condition than that in the simulated glaucoma. The alpha, beta, and gamma waves did not significantly differ among the four visual conditions (p＞0.05).
Conclusions: The results suggest that limited visual information negatively affects the ability to perform tasks requiring arm-hand dexterity and eye-hand coordination. However, the effectiveness of low visual acuity on the brainwaves should be further studied for rehabilitative evidence of visual impairment.
Miltiadous, Andreas; Aspiotis, Vasileios; Sakkas, Konstantinos; Giannakeas, Nikolaos; Glavas, Euripidis; Tzallas, Alexandros T
2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), IEEE 2022.
Stress is a subject always relevant to scientific research due to the numerous implications in human life. Typical biomarkers used in the physiological evaluation of stress include Electrocardiography, cortisol levels, galvanic skin response and other. Recently, one less widely used instrument for the assessment of stress that has been re-emerged due to advancements in computational power and machine learning techniques, is Electroencephalography. Moreover, as Virtual Reality HMDs are being rapidly adopted by the research community it becomes apparent that leveraging the offered advantages of VR for the exploration of stress can lead to novel controlable and reproducable experimental procedures. In this paper we combine EEG, ECG and the Perceived Stress Scale with a Virtual Reality phobia induction setting, to propose a protocol for assessing stress. The suggested protocol can be used for functional brain connectivity investigation and thus the evaluation of stress while it and can be expanded via the incorporation of machine learning algorithms for automatic stress level classification.
Rominger, Christian; Gubler, Dani`ele A; Makowski, Lisa M; Troche, Stefan J
In: International journal of psychophysiology, 2022.
Tags: DSI-24| |
The neurophysiological investigation of creative idea generation is a growing research area. EEG studies congruently reported the sensitivity of upper alpha power (10-12 Hz) for the creative ideation process and its outcome. However, the majority of studies were between-subject design studies and research directly comparing the neurophysiological activation pattern when generating more and less creative ideas within a person are rare. Therefore, the present study was specifically focused on investigating brain activation patterns associated with the generation of more vs. less creative ideas. We applied an alternate uses task (AU-task; i.e., finding original uses for everyday objects such as a brick) in a sample of 74 participants and recorded the brain activation during the AU-task and reference period. A portable EEG system with 21 dry electrodes arranged in the international 10–20 system and linked ear as reference was used. We found a higher increase of upper alpha power during creative ideation (relative to reference period, i.e., task-related power, TRP) over right posterior sites when people generated more compared to less creative ideas. This was accompanied by an increase of functional coupling (i.e., task-related coherence increase) between frontal and parietal/occipital sites, which suggests higher internal attention and more control over sensory processes. Taken together, these findings complement the existing creativity research literature and indicate the importance of alpha power for the creative ideation process also within people.
Dong, Xian; Wu, Yeyu; Tu, Zhijun; Cao, Bin; Li, Xianting; Yang, Zixu; Liu, Fei; Xing, Zheli
In: Energy and Buildings, pp. 112438, 2022.
Tags: DSI-7| |
When attacked by weapons of mass destruction, underground engineering will operate under isolation condition, which results in the increase of temperature, humidity, and CO2 concentration. At present, there are few studies on personnel thermal comfort and working efficiency in underground engineering, especially under isolation condition. To improve the personnel thermal comfort and working efficiency under such condition, the influence of environmental temperature changes on human thermal comfort and working efficiency was investigated through a combination of subjective and objective methods. A subjective questionnaire and working efficiency test were conducted on the subjects in the artificial climate chamber, and synchronously monitored electrocardiography (ECG), electroencephalography (EEG), and other physiological parameters of the subjects were recorded, when isolation condition was achieved in the artificial climate chamber. The results show that: (1) the human neutral temperature is 24.2 °C, and thermal comfort zone is [23 °C, 25.5 °C] for isolation condition; (2) the high working efficiency area is [27.3 °C, 28.8 °C] for isolation condition; (3) the average of the TSV corresponding to the highest working efficiency point is 1.3 under isolation condition; (4) from the correlation analysis of working efficiency and personnel physiological indicators, personnel EEG index and task performance are significantly related, and the ECG index and task performance are not relevant for subjects performing brain work under isolation conditions.
Hu, Yuxia; Wang, Yufei; Zhang, Rui; Hu, Yubo; Fang, Mingzhu; Li, Zhe; Shi, Li; Zhang, Yankun; Zhang, Zhong; Gao, Jinfeng; others,
In: Cognitive Neurodynamics, pp. 1–9, 2022.
The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (P < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C0 showed significant differences (P < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.
Won, Kyungho; Kim, Heegyu; Gwon, Daeun; Ahn, Minkyu; Nam, Chang S; Jun, Sung Chan
Brain-computer interface (BCI) has helped people by enabling them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and comparative study of different stimulation modalities has been overlooked. Accordingly, this comparative study was designed to investigate vibrotactile stimulation and transcranial direct current stimulation’s (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the BCI performance of low performers in the vibrotactile stimulation group increased significantly by 9.13% (p=0.0053), and while the tDCS group subjects’ performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking value (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers across all groups showed no significant stimulation effects. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users.
Michaelides, Andreas; Mitchell, Ellen Siobhan; Behr, Heather; Ho, Annabell Suh; Hanada, Grant; Lee, Jihye; McPartland, Sue
In: International Journal of Environmental Research and Public Health, vol. 19, 2022.
Tags: DSI-24| |
Executive functioning is a key component involved in many of the processes necessary for effective weight management behavior change (e.g., setting goals). Cognitive behavioral therapy (CBT) and third-wave CBT (e.g., mindfulness) are considered first-line treatments for obesity, but it is unknown to what extent they can improve or sustain executive functioning in a generalized weight management intervention. This pilot randomized controlled trial examined if a CBT-based generalized weight management intervention would affect executive functioning and executive function-related brain activity in individuals with obesity or overweight. Participants were randomized to an intervention condition (N = 24) that received the Noom Weight program or to a control group (N = 26) receiving weekly educational newsletters. EEG measurements were taken during Flanker, Stroop, and N-back tasks at baseline and months 1 through 4. After 4 months, the intervention condition evidenced greater accuracy over time on the Flanker and Stroop tasks and, to a lesser extent, neural markers of executive function compared to the control group. The intervention condition also lost more weight than controls (−7.1 pounds vs. +1.0 pounds). Given mixed evidence on whether weight management interventions, particularly CBT-based weight management interventions, are associated with changes in markers of executive function, this pilot study contributes preliminary evidence that a multicomponent CBT-based weight management intervention (i.e., that which provides both support for weight management and is based on CBT) can help individuals sustain executive function over 4 months compared to controls
Zeifman, Richard J; Spriggs, Meg J; Kettner, Hannes; Lyons, Taylor; Rosas, Fernando; Mediano, Pedro AM; Erritzoe, David; Carhart-Harris, Robin
Tags: DSI-24| |
Background:The Relaxed Beliefs Under pSychedelics (REBUS) modelproposes that serotonergic psychedelics decrease the precision weighting of neurobiologically-encoded beliefs, and offers a unified account of the acute and therapeutic action of psychedelics. AlthoughREBUShas received some neuroscientific support, little research has examined its psychological validity. We conducted a preliminary examination of two psychological assumptions of REBUS: (a) psychedelics foster acute relaxation and post-acute revision of confidence in mental-health-relevant beliefs; (b) this relaxation and revision facilitatespositive therapeutic outcomesand is associated with the entropy of EEG signals(anindex of neurophysiological mechanisms relevant to REBUS). Method:Healthy individuals (N=11) were administered 1 mg and 25 mgpsilocybin4-weeks apart. Confidence ratings forpersonally held negative and positive beliefswere obtainedbefore, during, and 4-weeks after dosing sessions. Acute entropyand self-reported subjective experiences were measured, as was well-being (before and 4-weeks after dosing sessions). Results:Confidence in negative self-beliefsdecreased following 25 mgpsilocybin and not following 1 mgpsilocybin. Entropy and subjective effects under 25 mgpsilocybincorrelated with decreases in negative self-belief confidence(acute and4-weeks after dosing). Particularlystrong evidence was seen for a relationship between decreases in negative self-belief confidence and increases in well-beingat 4-weeks.Conclusions:We reportthe first empirical evidence that therelaxation and revision of negative self-belief confidencemediatespositive psychological outcomes; a psychological assumption ofREBUS. Replication within larger and clinical samples remains necessary. We also introduce a newmeasure, the Relaxed BEliefs Questionnaire (REB-Q),forexaminingthe robustness of these preliminary findingsand the utility of the REBUSmode
Han, Chuanliang; Zhao, Xixi; Li, Meijia; Haihambo, Naem; Teng, Jiayi; Li, Sixiao; Qiu, Jinyi; Feng, Xiaoyang; Gao, Michel
In: Cognitive Neurodynamics, pp. 1–12, 2022.
Tags: DSI-24| |
Gamma-band activity was thought to be related to several high-level cognitive functions, and Gamma ENtrainment Using Sensory stimulation (GENUS, 40 Hz sensory combined visual and auditory stimulation) was found to have positive effects on patients with Alzheimer’s dementia. Other studies found, however, that neural responses induced by single 40 Hz auditory stimulation were relatively weak. To address this, we included several new experimental conditions (sounds with sinusoidal or square wave; open-eye and closed-eye state) combined with auditory stimulation with the aim of investigating which of these induces a stronger 40 Hz neural response. We found that when participant´s eyes were closed, sounds with 40 Hz sinusoidal wave induced the strongest 40 Hz neural response in the prefrontal region compared to responses in other conditions. More interestingly, we also found there is a suppression of alpha rhythms with 40 Hz square wave sounds. Our results provide potential new methods when using auditory entrainment, which may result in a better effect in preventing cerebral atrophy and improving cognitive performance.
Li, Jian; Maffei, Luigi; Pascale, Aniello; Masullo, Massimiliano
In: The Journal of the Acoustical Society of America, vol. 152, no. 1, pp. 172–183, 2022.
Tags: DSI-24| |
Informational masking of water sounds has been proven effective in mitigating traffic noise perception with different sound levels and signal-to-noise ratios, but less is known about the effects of the spatial distribution of water sounds on the perception of the surrounding environment and corresponding psychophysical responses. Three different spatial settings of water-sound sequences with a traffic noise condition were used to investigate the role of spatialization of water-sound sequences on traffic noise perception. The neural responses of 20 participants were recorded by a portable electroencephalogram (EEG) device during the spatial sound playback time. The mental effects and attention process related to informational masking were assessed by the analysis of the EEG spectral power distribution and sensor-level functional connectivity along with subjective assessments. The results showed higher relative power of the alpha band and greater alpha-beta ratio among water-sound sequence conditions compared to traffic noise conditions, which confirmed the increased relaxation on the mental state induced by the introduction of water sounds. Moreover, different spatial settings of water-sound sequences evoked different cognitive network responses. The setting of two-position switching water brought more attentional network activations than other water sequences related to the information masking process along with more positive subjective feelings
Humphries, Joseph B; Mattos, Daniela JS; Rutlin, Jerrel; Daniel, Andy GS; Rybczynski, Kathleen; Notestine, Theresa; Shimony, Joshua S; Burton, Harold; Carter, Alexandre; Leuthardt, Eric C
In: Brain-Computer Interfaces, vol. 9, no. 3, pp. 179–192, 2022.
Upper extremity weakness in chronic stroke remains a problem not fully addressed by current therapies. Brain–computer interfaces (BCIs) engaging the unaffected hemisphere are a promising therapy that are entering clinical application, but the mechanism underlying recovery is not well understood. We used resting state functional MRI to assess the impact a contralesionally driven EEG BCI therapy had on motor system functional organization. Patients used a therapeutic BCI for 12 weeks at home. We acquired resting-state fMRI scans and motor function data before and after the therapy period. Changes in functional connectivity (FC) strength between motor network regions of interest (ROIs) and the topographic extent of FC to specific ROIs were analyzed. Most patients achieved clinically significant improvement. Motor FC strength and topographic extent decreased following BCI therapy. Motor recovery correlated with reductions in motor FC strength across the entire motor network. These findings suggest BCI-mediated interventions may reverse pathologic strengthening of dysfunctional network interactions.
Kim, Min Gyu; Lim, Hyunmi; Lee, Hye Sun; Han, In Jun; Ku, Jeonghun; Kang, Youn Joo
In: Journal of Neural Engineering, vol. 19, no. 3, 2022.
Objective. Action observation (AO) combined with brain–computer interface (BCI) technology enhances cortical activation. Peripheral electrical stimulation (PES) increases corticospinal excitability, thereby activating brain plasticity. To maximize motor recovery, we assessed the effects of BCI-AO combined with PES on corticospinal plasticity. Approach. Seventeen patients with chronic hemiplegic stroke and 17 healthy subjects were recruited. The participants watched a video of repetitive grasping actions with four different tasks for 15 min: (A) AO alone; (B) AO + PES; (C) BCI-AO + continuous PES; and (D) BCI-AO + triggered PES. PES was applied at the ulnar nerve of the wrist. The tasks were performed in a random order at least three days apart. We assessed the latency and amplitude of motor evoked potentials (MEPs). We examined changes in MEP parameters pre-and post-exercise across the four tasks in the first dorsal interosseous muscle of the dominant hand (healthy subjects) and affected hand (stroke patients). Main results. The decrease in MEP latency and increase in MEP amplitude after the four tasks were significant in both groups. The increase in MEP amplitude was sustained for 20 min after tasks B, C, and D in both groups. The increase in MEP amplitude was significant between tasks A vs. B, B vs. C, and C vs. D. The estimated mean difference in MEP amplitude post-exercise was the highest for A and D in both groups. Significance. The results indicate that BCI-AO combined with PES is superior to AO alone or AO + PES for facilitating corticospinal plasticity in both healthy subjects and patients with stroke. Furthermore, this study supports the idea that synchronized activation of cortical and peripheral networks can enhance neuroplasticity after stroke. We suggest that the BCI-AO paradigm and PES could provide a novel neurorehabilitation strategy for patients with stroke.
Rustamov, Nabi; Humphries, Joseph; Carter, Alexandre; Leuthardt, Eric C
In: Brain Communications, vol. 4, iss. 3, 2022.
Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain–computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain–computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain–computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain–computer interface therapy are associated with alterations in phase–amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain–computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain–computer interface therapy and once every 4 weeks after the therapy. Changes in phase–amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta–gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain–computer interface therapy sessions. Importantly, an increase in theta–gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta–gamma coupling increase following brain–computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta–gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha–gamma coupling was not altered by brain–computer interface therapy. Power spectra did not change significantly over the course of the brain–computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain–computer interface therapy was strongly correlated with increased theta–gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.
Klee, Daniel; Memmott, Tab; Smedemark-Margulies, Niklas; Celik, Basak; Erdogmus, Deniz; Oken, Barry S
In: Frontiers in Human Neuroscience, vol. 16, 2022.
This study evaluated the feasibility of using occipitoparietal alpha activity to drive target/non-target classification in a brain-computer interface (BCI) for communication. EEG data were collected from 12 participants who completed BCI Rapid Serial Visual Presentation (RSVP) calibrations at two different presentation rates: 1 and 4 Hz. Attention-related changes in posterior alpha activity were compared to two event-related potentials (ERPs): N200 and P300. Machine learning approaches evaluated target/non-target classification accuracy using alpha activity. Results indicated significant alpha attenuation following target letters at both 1 and 4 Hz presentation rates, though this effect was significantly reduced in the 4 Hz condition. Target-related alpha attenuation was not correlated with coincident N200 or P300 target effects. Classification using posterior alpha activity was above chance and benefitted from individualized tuning procedures. These findings suggest that target-related posterior alpha attenuation is detectable in a BCI RSVP calibration and that this signal could be leveraged in machine learning algorithms used for RSVP or comparable attention-based BCI paradigms.
Kim, Nayeon; Gero, John S
This pilot study explores the effects of biophilic design on university students’ neurophysiological responses in virtual classrooms through measuring relative alpha and beta power using EEG in two different display conditions: a conventional computer display and an immersive VR HeadMounted Display. Seventeen male undergraduate students from both a design major and a non-design major in their twenties at Yonsei University
participated. Seven different biophilic design cases were presented as visual
stimuli to participants in the two different conditions. Results of ANOVA
analysis revealed significant main effects of condition and hemisphere in the
relative alpha power. Results revealed there is significant interaction effect
between case and major as well as between condition, case, hemisphere, and
major in relative beta power. Results showed statistically significant differences in some electrodes of both relative alpha and relative beta measurements between some cases when presented in the computer display. In the
VR presentation, differences were found only in the relative beta in some
electrodes. This study has the potential to contribute to building evidencebased design strategies for improving biophilic design environments.
Schneefeld, F; Doelling, K; Marchesotti, S; Schwartz, S; Igloi, K; Giraud, AL; Arnal, LH
In: bioRxiv, 2022.
The human auditory system is not equally reactive to all frequencies of the audible spectrum. Emotional and behavioral reactions to loud or aversive acoustic features can vary from one individual to another, to the point that some exhibit exaggerated or even pathological responses to certain sounds. The neural mechanisms underlying these interindividual differences remain unclear. Whether distinct aversion profiles map onto neural excitability at the individual level needs to be tested. Here, we measured behavioral and EEG responses to click trains (from 10 to 250 Hz, spanning the roughness and pitch perceptual ranges) to test the hypothesis that interindividual variability in aversion to rough sounds is reflected in neural response differences between participants. Linking subjective aversion to 40 Hz steady-state EEG responses, we demonstrate that participants experiencing enhanced aversion to roughness also show stronger neural responses to this attribute. Interestingly, this pattern also correlates with inter-individual anxiety levels, suggesting that this personality trait might interact with subjective sensitivity and neural excitability to these sounds. These results support the idea that 40 Hz sounds can probe the excitability of non-canonical auditory systems involved in exogenous salience processing and aversive responses at the individual level. By linking subjective aversion to neural excitability, 40 Hz sounds provide neuromarkers relevant to a variety of pathological conditions, such as those featuring enhanced emotional sensitivity (hyperacusis, anxiety) or aberrant neural responses at 40 Hz (autism, schizophrenia).
Haro, Stephanie; Rao, Hrishikesh M; Quatieri, Thomas F; Smalt, Christopher J
In: European Journal of Neuroscience, vol. 55, no. 5, pp. 1262–1277, 2022.
Tags: DSI-24| |
Everyday environments often contain distracting competing talkers and background noise, requiring listeners to focus their attention on one acoustic source and reject others. During this auditory attention task, listeners may naturally interrupt their sustained attention and switch attended sources. The effort required to perform this attention switch has not been well studied in the context of competing continuous speech. In this work, we developed two variants of endogenous attention switching and a sustained attention control. We characterized these three experimental conditions under the context of decoding auditory attention, while simultaneously evaluating listening effort and neural markers of spatial-audio cues. A least-squares, electroencephalography (EEG)-based, attention decoding algorithm was implemented across all conditions. It achieved an accuracy of 69.4% and 64.0% when computed over nonoverlapping 10 and 5-s correlation windows, respectively. Both decoders illustrated smooth transitions in the attended talker prediction through switches at approximately half of the analysis window size (e.g., the mean lag taken across the two switch conditions was 2.2 s when the 5-s correlation window was used). Expended listening effort, as measured by simultaneous EEG and pupillometry, was also a strong indicator of whether the listeners sustained attention or performed an endogenous attention switch (peak pupil diameter measure [ ] and minimum parietal alpha power measure [ ]). We additionally found evidence of talker spatial cues in the form of centrotemporal alpha power lateralization ( ). These results suggest that listener effort and spatial cues may be promising features to pursue in a decoding context, in addition to speech-based features.
Gubler, Dani`ele A; Zeiss, Stephan; Egloff, Niklaus; Frickmann, Frank; Goetze, Benjamin; Harnik, Michael; Streitberger, Konrad; Troche, Stefan J; others,
In: Behavioral neuroscience, vol. 136, no. 2, pp. 195, 2022.
Tags: DSI-24| |
Although the interrupting effect of chronic pain on voluntary-directed attention is well-documented, research on the impact of chronic pain on involuntary-directed attention remains incomplete. This study aimed to investigate the influence of chronic pain on involuntary as well as voluntary allocation of attention as, respectively, indexed by the P3a and P3b components in the event-related potential derived from the electroencephalogram. Both involuntary and voluntary captures of attention were compared between 33 patients with chronic pain and 33 healthy controls using an auditory three-stimulus oddball task (with standard, target, and unexpected distractor tones). The results revealed a reduced P3a amplitude as well as a reduced P3b amplitude in patients with chronic pain compared to healthy controls, indicating a detrimental effect of chronic pain on involuntary and voluntary attention, respectively. This study extends the picture of the impairing effects of chronic pain on attentional allocation to a current task and attentional allocation to information outside the focus of attention. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
Arakaki, Xianghong; Hung, Shao-Min; Rochart, Roger; Fonteh, Alfred N; Harrington, Michael G
In: Neurobiology of Aging, 2021.
Tags: DSI-24| |
Synaptic dysfunctions precede cognitive decline in Alzheimer's disease (AD) by decades, affect executive functions, and can be detected by quantitative electroencephalography (qEEG). We used qEEG combined with Stroop testing to identify changes of inhibitory controls in cognitively healthy individuals with an abnormal versus normal ratio of cerebrospinal fluid (CSF) amyloid/total-tau. We studied two groups of participants (60-94 years) with either normal (CH-NAT or controls, n = 20) or abnormal (CH-PAT, n = 21) CSF amyloid/tau ratio. We compared: alpha event-related desynchronization (ERD), alpha spectral entropy (SE), and their relationships with estimated cognitive reserve. CH-PATs had more negative occipital alpha ERD, and higher frontal and occipital alpha SE during low load congruent trials, indicating hyperactivity. CH-PATs demonstrated fewer frontal SE changes with higher load, incongruent Stroop testing. Correlations of alpha ERD with estimated cognitive reserve were significant in CH-PATs but not in CH-NATs. These results suggested compensatory hyperactivity in CH-PATs compared to CH-NATs. We did not find differences in alpha ERD comparisons with individual CSF amyloid(A), p-tau(T), total-tau(N) biomarkers.
Dong, Sunghee; Jin, Yan; Bak, SuJin; Yoon, Bumchul; Jeong, Jichai
In: Electronics, vol. 10, no. 23, pp. 3020, 2021.
Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults (24.5±2.7 years) and 12 older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly
Arechavala, Rebecca Johnson; Rochart, Roger; Kloner, Robert A; Liu, Anqi; Wu, Daw-An; Hung, Shao-Min; Shimojo, Shinsuke; Fonteh, Alfred N; Kleinman, Michael T; Harrington, Michael G; others,
In: International Journal of Psychophysiology, vol. 170, pp. 102–111, 2021.
Tags: DSI-24| |
Electroencephalographic (EEG) alpha oscillations have been related to heart rate variability (HRV) and both change in Alzheimer's disease (AD). We explored if task switching reveals altered alpha power and HRV in cognitively healthy individuals with AD pathology in cerebrospinal fluid (CSF) and whether HRV improves the AD pathology classification by alpha power alone.
We compared low and high alpha event-related desynchronization (ERD) and HRV parameters during task switch testing between two groups of cognitively healthy participants classified by CSF amyloid/tau ratio: normal (CH-NAT, n = 19) or pathological (CH-PAT, n = 27). For the task switching paradigm, participants were required to name the color or word for each colored word stimulus, with two sequential stimuli per trial. Trials include color (cC) or word (wW) repeats with low load repeating, and word (cW) or color switch (wC) for high load switching. HRV was assessed for RR interval, standard deviation of RR-intervals (SDNN) and root mean squared successive differences (RMSSD) in time domain, and low frequency (LF), high frequency (HF), and LF/HF ratio in frequency domain.
Results showed that CH-PATs compared to CH-NATs presented: 1) increased (less negative) low alpha ERD during low load repeat trials and lower word switch cost (low alpha: p = 0.008, Cohen's d = −0.83, 95% confidence interval −1.44 to −0.22, and high alpha: p = 0.019, Cohen's d = −0.73, 95% confidence interval −1.34 to −0.13); 2) decreasing HRV from rest to task, suggesting hyper-activated sympatho-vagal responses. 3) CH-PATs classification by alpha ERD was improved by supplementing HRV signatures, supporting a potentially compromised brain-heart interoceptive regulation in CH-PATs. Further experiments are needed to validate these findings for clinical significance.
McLaughlin, Deirdre; Klee, Daniel; Memmott, Tab; Peters, Betts; Wiedrick, Jack; Fried-Oken, Melanie; Oken, Barry
In: Human-Computer Interaction, 2021.
Brain computer interfaces systems are controlled by users through neurophysiological input for a variety of applications including communication, environmental control, motor rehabilitation, and cognitive training. Although individuals with severe speech and physical impairment are the primary users of this technology, BCIs have emerged as a potential tool for broader populations, especially with regards to delivering cognitive training or interventions with neurofeedback. The goal of this study was to investigate the feasibility of using a BCI system with neurofeedback as an intervention for people with mild Alzheimer's disease. The study focused on visual attention and language since ad is often associated with functional impairments in language and reading. The study enrolled five adults with mild ad in a nine to thirteen week BCI EEG based neurofeedback intervention to improve attention and reading skills. Two participants completed intervention entirely. The remaining three participants could not complete the intervention phase because of restrictions related to covid. Pre and post assessment measures were used to assess reliability of outcome measures and generalization of treatment to functional reading, processing speed, attention, and working memory skills. Participants demonstrated steady improvement in most cognitive measures across experimental phases, although there was not a significant effect of NFB on most measures of attention. One subject demonstrated significantly significant improvement in letter cancellation during NFB. All participants with mild AD learned to operate a BCI system with training. Results have broad implications for the design and use of bci systems for participants with cognitive impairment. Preliminary evidence justifies implementing NFB-based cognitive measures in AD.
Jung, Mijung; Lee, Mikyoung
In: Healthcare 2021, vol. 9, no. 11, pp. 1606, 2021.
Background: Mindfulness, defined as the awareness emerging from purposefully paying attention to the present moment, has been shown to be effective in reducing stress and, thus, promoting psychological well-being. This study investigated the effects of a mindfulness-based education program on mindfulness, brain waves, and the autonomic nervous system (ANS) in university students in Korea. Methods: This study is a quantitative and experimental research with a single-group pre-post design. Six sessions of mindfulness-based intervention were applied. In total, 42 students completed a mindfulness questionnaire before and after the intervention, and 28 among them completed pre-intervention and post-intervention measures of brain waves and ANS. Results: The level of mindfulness increased in the participants after intervention. Regarding brain waves, the alpha and theta waves increased, but the beta waves decreased. There was no significant difference in the ANS, presenting no change in heart rate variability. Conclusions: We identified the positive effects of the mindfulness-based education program for university students. The findings indicate that this program may help students not only relax, but also generate a mindfulness state in stressful situations, potentially leading to a successful university life. This study can be used as a basis for quality improvement and sustainability of mindfulness-based education programs for university students.
In: Journal of Convergence for Information Technology, vol. 11, no. 10, pp. 10–19, 2021.
In this paper, we tried to derive characteristic parameters that reflect mental fatigue through EEG measurement and analysis. For this purpose, mental fatigue was induced through a resting state with eyes closed and performing subtraction operations in mental arithmetic for 30 minutes. Five subjects participated in the experiment, and all subjects were right-handed male students in university, with an average age of 25.5 years. Spectral analysis was performed on the EEG collected at the beginning and the end of the experiment to derive feature parameters reflecting mental fatigue. As a result of the analysis, the absolute power of the alpha band in the occipital lobe and the temporal lobe increased as the mental fatigue increased, while the relative power decreased. Also, the difference in power between resting state and task state showed that the relative power was larger than the absolute power. These results indicate that alpha relative power in the occipital lobe and temporal lobe is a feature parameter reflecting mental fatigue. The results of this study can be utilized as feature parameters for the development of an automated system for mental fatigue determination such as fatigue and drowsiness while driving.
Chakravarty, Sumit; Xie, Ying; Le, Linh; Johnson, John; Hales, Michael
International Conference on Brain Informatics, vol. 12960, Springer 2021, ISBN: 978-3-030-86993-9.
A person’s state of attentiveness can be affected by various outside factors. Having energy, feeling tired, or even simply being distracted all play a role in someone’s level of attention. The task at hand can potentially affect the person’s attention or concentration level as well. In terms of students who take online courses, constantly watching lectures and conducting these courses solely online can cause lack of concentration or attention. Attention can be considered in two categories: passive or active. Conducting active and passive attention-based trials can reveal different states of attentiveness. This paper compares active and passive attention trial results of the two states, wide awake and tired. This has been done in order to uncover a difference in results between the two states. The data analyzed throughout this paper was collected from DSI 24 EEG equipment, and the generated EEG is processed through a 3D Convolutional Neural Network (CNN) to produce results. Three passive attention trials and three active attention trials were performed on seven subjects, while they were wide awake and when they were tired. The experiments on the preprocessed data results in accuracies as high as 81.78% for passive attention detection accuracy and 63.67% for active attention detection accuracy, which shown a clear ability to separate between the two attention categories.
Snider, Dallas H; Linnville, Steven E; Phillips, Jeffrey B; Rice, Merrill G
In: Biomedical Signal Processing and Control, vol. 71, pp. 103110, 2021.
The capability of detecting symptoms of hypoxia (i.e., reduced oxygen) and other cognitive impairments in-flight with wearable sensors and machine learning based algorithms will benefit the aviation community by saving lives and preventing mishaps. In this study, knowledge discovery processes were implemented to build classification models to predict hypoxia from wearable, dry-EEG sensor data collected from 85 participants in a two-phase study. Over a 35-minute period and while wearing aviation flight masks which regulated their oxygen intake, participants would alternate between a 2-minute cognitive test on CogScreen Hypoxia Edition and a 3-minute simulated flying task on X-Plane 11, with the oxygen concentration reducing every 5 min following the simulated flight task. The decrease in oxygen each 5 min simulated an increase in altitude. Features extracted from the EEG waveforms were transformed using principal component analysis to reduce the dimensionality of the data. Naïve Bayes, decision tree, random forest, and neural network algorithms were utilized to classify the transformed brain wave data as either normal or hypoxic. The algorithms sensitivity ranged from 0.83 to 1.00 while the specificity ranged from 0.91 to 1.00. This study makes a step forward in developing a real-time, in-flight hypoxia detection system.
Kim, Soram; Lee, Seungyun; Kang, Hyunsuk; Kim, Sion; Ahn, Minkyu
In: Sensors, vol. 21, no. 17, pp. 5765, 2021.
Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain–computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations
Lim, Hyunmi; Ku, Jeonghun
In: Cyberpsychology, Behavior, and Social Networking, vol. 24, no. 8, pp. 566–572, 2021.
Action observation (AO) is a promising strategy for promoting motor function in neural rehabilitation. Recently, brain–computer interface (BCI)-AO game rehabilitation, which combines AO therapy with BCI technology, has been introduced to improve the effectiveness of rehabilitation. This approach can improve motor learning by providing feedback, which can be interactive in an observation task, and the game contents of the BCI-AO game paradigm can affect rehabilitation. In this study, the effects of congruent rather than incongruent feedback in a BCI-AO game on mirror neurons were investigated. Specifically, the mu suppression with congruent and incongruent BCI-AO games was measured in 17 healthy adults. The mu suppression in the central motor cortex was significantly higher with the congruent BCI-AO game than with the incongruent one. In addition, the satisfaction evaluation results were excellent for the congruent case. These results support the fact that providing feedback congruent with the motion of an action video facilitates mirror neuron activity and can offer useful guidelines for the design of BCI-AO games for rehabilitation
Lin, Chun-Ling; Hsieh, Ya-Wen; Chen, Hui-Ya
In: Behavioural Brain Research, vol. 408, pp. 113279, 2021.
Sensory challenges to postural balance are daily threats for elderly individuals. This study examined electroencephalography (EEG) in alpha and beta bands in sensory association areas during the Sensory Organization Test, involving withdrawal of visual or presenting misleading somatosensory inputs, in twelve young and twelve elderly participants. The results showed stepwise deterioration in behavioral performance in four conditions, with group effects that were amplified with combined sensory challenges. With eye closure, alpha and beta activities increased in all sensory association areas. Fast beta activity increased in the bilateral parietal-temporal-occipital areas. Misleading somatosensory information effects on EEG activity were of smaller amplitude than eye closure effects and in a different direction. Decreased alpha activity in left parietal-temporal-occipital areas and decreased beta and fast beta activities in bilateral parietal-temporal-occipital areas were significant. Elderly participants had increased fast beta activity in the left temporal-occipital and bilateral occipital areas, indicative of sustained efforts that they made in all sensory conditions. Similar to the young participants, elderly participants with eyes closed showed increased alpha activity, although to a smaller degree, in bilateral temporal-occipital and left occipital areas. This might indicate a lack of efficacy in redistributing relative sensory weights when elderly participants dealt with eye closure. In summary, EEG power changes did not match the stepwise deterioration in behavioral data, but reflected different sensory strategies adopted by young and elderly participants to cope with eye closure or misleading somatosensory information based on the efficacy of these different strategies.
Swerdloff, Margaret M; Hargrove, Levi J
2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE 2021, ISBN: 978-1-7281-4338-5.
Tags: DSI-7| |
Assessing cognitive load may be useful in a variety of applications, especially in identifying the onset of high cognitive load. However, current methods do not exist that can pinpoint such an event. We recorded EEG from participants while they completed auditory oddball paradigm and Stroop tasks. To determine the time at which a change in cognitive load could be detected, we applied auditory tones at four time points before and after the onset of an incongruent Stroop trial: (1) 100 ms prior to the Stroop onset, (2) 100 ms post Stroop onset, (3) 300 ms post Stroop onset, and (4) 450 ms post Stroop onset. Event-related potential results suggest that cognitive load was highest at time points after 100 ms post Stroop onset. This work provides a method for identifying an increase in cognitive load, which could be an important diagnostic tool for device development and tuning.
Fan, Chen-Chen; Xie, Haiqun; Peng, Liang; Yang, Hongjun; Ni, Zhen-Liang; Wang, Guanán; Zhou, Yan-Jie; Chen, Sheng; Fang, Zhijie; Huang, Shuyun; others,
2021 International Conference on Robotics and Automation, 2021.
Tags: DSI-24| |
Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.
Kim, Sanghee; Park, Hyejin; Choo, Seungyeon
In: International Journal of Environmental Research and Public Health, vol. 18, no. 8, pp. 4305, 2021.
This study combines electroencephalogram (EEG) with virtual reality (VR) technologies to measure the EEG responses of users experiencing changes to architectural elements. We analyze the ratio of alpha to beta waves (RAB) indicators to determine the pre- and poststimulation changes. In our methodology, thirty-three females experience using private rooms in a postpartum care center participated in the experiment. Their brain waves are measured while they are experiencing the VR space of a private room in a postpartum care center. Three architectural elements (i.e., aspect ratio of space, ceiling height, and window ratio) are varied in the VR space. In addition, a self-report questionnaire is administered to examine whether the responses are consistent with the results of the EEG response analysis. As a result, statistically significant differences (p < 0.05) are observed in the changes in the RAB indicator values of the pre- and poststimulation EEG while the subjects are experiencing the VR space where the architectural elements are varied. That is, the effects of the changes to architectural elements on users’ relaxation-arousal responses are statistically verified. Notably, in all the RAB indicator values where significant differences are observed, the poststimulation RAB decreases in comparison to the prestimulus ratios, which is indicative of the arousal response. However, the arousal levels vary across the architectural elements, which implies it would be possible to find out the elements that could induce less arousal response using the proposed method. Moreover, following the experience in the VR space, certain lobes of the brain (F4 and P3 EEG channels) show statistically significant differences in the relaxation-arousal responses. Unlike previous studies, which measured users’ physiological responses to abstract and primordial spatial elements, this study extends the boundaries of the literature by applying the architectural elements applicable to design in practice
Human Brain and Artificial Intelligence: Second International Workshop, HBAI 2020, Held in Conjunction with IJCAI-PRICAI 2020, Yokohama, Japan, January 7, 2021, Revised Selected Papers, Springer Nature 2021.
The SSVEP-BCI system usually uses a fixed calculation time and a static window stop method to decode the EEG signal, which reduces the efficiency of the system. In response to this problem, this paper uses an adaptive FBCCA algorithm, which uses Bayesian estimation to dynamically find the optimal data length for result prediction, adapts to the differences between different trials and different individuals, and effectively improves system operation effectiveness. At the same time, through this method, this paper constructs a brain-controlled robotic arm grasping life assistance system based on adaptive FBCCA. In this paper, we selected 20 subjects and conducted a total of 400 experiments. A large number of experiments have verified that the system is available and the average recognition success rate is 95.5%. This also proves that the system can be applied to actual scenarios. Help the handicapped to use the brain to control the mechanical arm to grab the needed items to assist in daily life and improve the quality of life. In the future, SSVEP’s adaptive FBCCA decoding algorithm can be combined with the motor imaging brain-computer interface decoding algorithm to build a corresponding system to help patients with upper or lower limb movement disorders caused by stroke diseases to recover, and reshape the brain and Control connection of limbs.
Lim, Hyunmi; Ku, Jeonghun
2021 9th International Winter Conference on Brain-Computer Interface (BCI), IEEE 2021, ISBN: 978-1-7281-8486-9.
In this study, we examined that neurofeedback training encouraging to make mu suppression over the motor cortex would effect on the brain activation of the motor cortex while performing hand movements. To investigate the effects of training with neurofeedback, we analyzed the pattern changes of the amount of the mu suppression during real hand movements just after every neurofeedback training. The healthy subjects were trained to increase the motor cortex activity by using motor imagery, specifically maximizing the difference of the mu power between C3 and C4 during neurofeedback training. We have observed that the changes of the mu suppression over the motor cortex become stronger as the neurofeedback training was repeated. These findings further suggest that motor imagery training using neurofeedback can be applied to patients with stroke or chronic neurological disorders.
Eldeeb, Safaa; Susam, Busra T; Akcakaya, Murat; Conner, Caitlin M; White, Susan W; Mazefsky, Carla A
In: Scientific Reports, vol. 11, no. 1, pp. 1–13, 2021.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%).
Memmott, Tab; Koçanaoğullari, Aziz; Lawhead, Matthew; Klee, Daniel; Dudy, Shiran; Fried-Oken, Melanie; Oken, Barry
BciPy: brain--computer interface software in Python Journal Article
In: Brain-Computer Interfaces, pp. 1-18, 2021.
There are high technological and software demands associated with conducting Brain–Computer Interface (BCI) research. In order to accelerate the development and accessibility of BCIs, it is worthwhile to focus on open-source and community desired tooling. Python, a prominent computer language, has emerged as a language of choice for many research and engineering purposes. In this article, BciPy, an open-source, Python-based software for conducting BCI research is presented. It was developed with a focus on restoring communication using Event-Related Potential (ERP) spelling interfaces; however, it may be used for other non-spelling and non-ERP BCI paradigms. Major modules in this system include support for data acquisition, data queries, stimuli presentation, signal processing, signal viewing and modeling, language modeling, task building, and a simple Graphical User Interface (GUI).
Rahimi-Nasrabadi, Hamed; Jin, Jianzhong; Mazade, Reece; Pons, Carmen; Najafian, Sohrab; Alonso, Jose-Manuel
Image luminance changes contrast sensitivity in visual cortex Journal Article
In: Cell reports, vol. 34, no. 5, pp. 108692, 2021.
Tags: DSI-7| |
Accurate measures of contrast sensitivity are important for evaluating visual disease progression and for navigation safety. Previous measures suggested that cortical contrast sensitivity was constant across widely different luminance ranges experienced indoors and outdoors. Against this notion, here, we show that luminance range changes contrast sensitivity in both cat and human cortex, and the changes are different for dark and light stimuli. As luminance range increases, contrast sensitivity increases more within cortical pathways signaling lights than those signaling darks. Conversely, when the luminance range is constant, light-dark differences in contrast sensitivity remain relatively constant even if background luminance changes. We show that a Naka-Rushton function modified to include luminance range and light-dark polarity accurately replicates both the statistics of light-dark features in natural scenes and the cortical responses to multiple combinations of contrast and luminance. We conclude that differences in light-dark contrast increase with luminance range and are largest in bright environments.
Neilson, Brittany N; Phillips, Jeffrey B; Snider, Dallas H; Drollinger, Sabrina M; Linnville, Steven E; Mayes, Ryan S
2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID), IEEE IEEE, Miramar Beach, FL, USA, 2020, ISBN: 978-1-7281-5890-7.
Changes in brainwave activity have been associated with hypoxia, but the literature is inconsistent. Twenty-five participants were subjected to normobaric hypoxia while undergoing a variety of cognitive tasks. The detected differences in brain activity between normal and hypoxic conditions are presented.
Haar Millo, S; Faisal, A
In: Frontiers in Human Neuroscience, vol. 14, pp. 354, 2020, ISBN: 1662-5161.
Tags: DSI-24| |
Many recent studies found signatures of motor learning in neural beta oscillations (13–30 Hz), and specifically in the post-movement beta rebound (PMBR). All these studies were in controlled laboratory-tasks in which the task designed to induce the studied learning mechanism. Interestingly, these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based and reward-based tasks (increase vs. decrease, respectively). Here, we explored the PMBR dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. Our EEG recordings highlight the opposing dynamics of PMBR magnitudes (increase vs. decrease) between different subjects performing the same task. The groups of subjects, defined by their neural dynamics, also showed behavioral differences expected for different learning mechanisms. Our results suggest that when faced with the complexity of the real-world different subjects might use different learning mechanisms for the same complex task. We speculate that all subjects combine multi-modal mechanisms of learning, but different subjects have different predominant learning mechanisms.
Kim, Young-June; Park, Jin-Hong; Cho, Young-Suk; Kim, Keum-Sook
The Effect of Cognitive Rehabilitation Program Using Virtual Reality (VR) Contents on Cognitive function, Depression, Upper Extremity Function and Activities of Daily Living in the Elderly Journal Article
In: Journal of Convergence for Information Technology, vol. 10, no. 8, pp. 203–212, 2020.
The purpose of this study was to investigate the effects of cognitive rehabilitation programs using Virtual Reality(VR) content on the daily living abilities such as cognitive abilities, depression, and upper extremity functions of the elderly. The study group analyzed the effectiveness by separating the experimental group, which is the virtual reality cognitive rehabilitation application group, and the control group, the universal cognitive stimulation program application group. As a result of the study, the MMSE-K score improved by 13.0% in the experimental group and 2.3% in the control group. The improvement in each area of the experimental group was found to be 3.1% MBI, 7.1% MFT(Rt.), 3.5% MFT(Lt.), and 25.4% K-GDS. As a result of comparing the pre-post score change between each group, there was a significant difference between groups in daily living ability (p<.001) and MFT(Rt.)(p<.01). In addition, as a result of comparing the changes in absolute alpha waves to confirm the degree of depression through brain waves, there was no statistically significant difference. However, in the experimental group, it was confirmed that the average value increased to a positive value. This study is an experiment to verify the effectiveness of the cognitive rehabilitation program using virtual reality contents, and suggests a new intervention method to maintain and improve the daily life ability, cognitive function, depression and upper extremity function of the elderly.
Lim, Hyunmi; Kim, Won-Seok; Ku, Jeonghun
In: Cyberpsychology, Behavior, and Social Networking, vol. 23, no. 8, pp. 541–549, 2020.
Virtual reality (VR) is effectively used to evoke the mirror illusion, and transcranial direct current stimulation (tDCS) synergistically facilitates this illusion. This study investigated whether a mirror virtual hand illusion (MVHI) induced by an immersive, first-person-perspective, virtual mirror system could be modulated by tDCS of the primary motor cortex. Fourteen healthy adults (average age 21.86 years ±0.47, seven men and seven women) participated in this study, and they experienced VR with and without tDCS—the tDCS and sham conditions, each of which takes ∼30 minutes—on separate days to allow the washout of the tDCS effect. While experiencing VR, the movements of the virtual left hand reflected the flexion and extension of the real right hand. Subsequently, electroencephalogram was recorded, the magnitude of the proprioceptive shift was measured, and the participants provided responses to a questionnaire regarding hand ownership. A significant difference in the proprioceptive shift was observed between the tDCS and sham conditions. In addition, there was significant suppression of the mu power in Pz, and augmentation of the beta power in the Pz, P4, O1, and O2 channels. The difference in proprioceptive deviation between the two conditions showed significant negative correlation with mu suppression over the left frontal lobe in the tDCS condition. Finally, the question “I felt that the virtual hand was my own hand” received a significantly higher score under the tDCS condition. In short, applying tDCS over the motor cortex facilitates the MVHI by activating the attentional network over the parietal and frontal lobes such that the MVHI induces more proprioceptive drift, which suggests that the combination of VR and tDCS can enhance the immersive effect in VR. This result provides better support for the use of the MVHI paradigm in combination with tDCS for recovery from illnesses such as stroke.
Son, Ji Eun; Choi, Hyoseon; Lim, Hyunmi; Ku, Jeonghun
Development of a flickering action video based steady state visual evoked potential triggered brain computer interface-functional electrical stimulation for a rehabilitative action observation game Journal Article
In: Technology and Health Care, vol. 28, no. S1, pp. 509-519, 2020.
This study focused on developing an upper limb rehabilitation program. In this regard, a steady state visual evoked potential (SSVEP) triggered brain computer interface (BCI)-functional electrical stimulation (FES) based action observation game featuring a flickering action video was designed.
In particular, the synergetic effect of the game was investigated by combining the action observation paradigm with BCI based FES.
The BCI-FES system was contrasted under two conditions: with flickering action video and flickering noise video. In this regard, 11 right-handed subjects aged between 22–27 years were recruited. The differences in brain activation in response to the two conditions were examined.
The results indicate that T3 and P3 channels exhibited greater Mu suppression in 8–13 Hz for the action video than the noise video. Furthermore, T4, C4, and P4 channels indicated augmented high beta (21–30 Hz) for the action in contrast to the noise video. Finally, T4 indicated suppressed low beta (14–20 Hz) for the action video in contrast to the noise video.
The flickering action video based BCI-FES system induced a more synergetic effect on cortical activation than the flickering noise based system.
Wang, Jiahui; Antonenko, Pavlo; Keil, Andreas; Dawson, Kara
In: Mind, Brain, and Education, vol. 14, no. 3, pp. 279–291, 2020.
Many online videos feature an instructor on the screen to improve learners' engagement; however, the influence of this design on learners' cognitive load is underexplored. This study investigates the effects of instructor presence on learners' processing of information using both subjective and psychophysiological measures of cognitive load. Sixty university students watched a statistics instructional video either with or without instructor presence, while the spontaneous electrical activity of their brain was recorded using electroencephalography (EEG). At the conclusion of the video, they also self-reported overall load, intrinsic load, extraneous load, and germane load they experienced during the video. Learning from the video was assessed via tests of retention and transfer. Results suggested the instructor-present video improved learners' ability to transfer information and was associated with a lower self-reported intrinsic and extraneous load. Event-related changes in theta band activity also indicated lower cognitive load with instructor-present video.
Mahdid, Yacine; Lee, Uncheol; Blain-Moraes, Stefanie
In: IEEE Access, vol. 8, pp. 193214–193225, 2020, ISSN: 2169-3536.
Assessing the data quality of wearable electroencephalogram (EEG) systems is critical to collecting reliable neurophysiological data in non-laboratory environments. To date, measures of signal quality and spectral characteristics have been used to characterize wearable EEG systems. We demonstrate that these traditional measures do not provide fine-grained differentiation between the performance of four popular wearable EEG systems (the Epoc+, OpenBCI, DSI-24 and Quick-30 Dry EEG). Using two computationally inexpensive metrics of undirected functional connectivity (phase lag index) and directed functional connectivity (directed phase lag index), we compare the integrity of the phase relationships captured by wearable systems to those recorded from a high-density research-grade EEG system (Electrical Geodesics Inc). Our results demonstrate that functional connectivity analyses provide additional discriminatory information about wearable EEG systems, with clear differentiation of the cosine similarity between research-grade functional connectivity patterns and those generated by each wearable system. We provide a freely available Matlab toolbox containing all metrics described in this paper such that researchers and non-experts interested in wearable EEG systems can easily assess the quality of systems not characterized in this study, thus advancing the translation of EEG research into non-laboratory settings.
Islam, Md Shafiqul; El-Hajj, Ahmad M; Alawieh, Hussein; Dawy, Zaher; Abbas, Nabil; El-Imad, Jamil
In: Biomedical Signal Processing and Control, vol. 55, pp. 101638, 2020, ISSN: 1746-8094.
Mobile monitoring of electroencephalogram (EEG) signals is prone to different sources of artifacts. Most importantly, motion-related artifacts present a major challenge hindering the clean acquisition of EEG data as they spread all over the scalp and across all frequency bands. This leads to additional complexity in the development of neurologically-oriented mobile health solutions. Among the top five most common neurological disorders, epilepsy has increasingly relied on EEG for diagnosis. Separate methods have been used to classify EEG segments in the context of epilepsy while reducing the existing mobility artifacts. This work specifically devises an approach to remove motion-related artifacts in the context of epilepsy. The proposed approach first includes the recording of EEG signals using a wearable EEG headset. The recorded signals are then colored by some motion artifacts generated in a lab-controlled experiment. This stage is followed by temporal and spectral characterization of the signals and artifact removal using independent component analysis (ICA). The proposed approach is tested using real clinical EEG data and results showed an average increase in accuracy of ∼9% in seizure detection and ∼24% in prediction.
Choi, Hyoseon; Lim, Hyunmi; Kim, Joon Woo; Kang, Youn Joo; Ku, Jeonghun
In: Electronics, vol. 8, no. 12, pp. 1466, 2019.
Action observation (AO), based on the mirror neuron theory, is a promising strategy to promote motor cortical activation in neurorehabilitation. Brain computer interface (BCI) can detect a user’s intention and provide them with brain state-dependent feedback to assist with patient rehabilitation. We investigated the effects of a combined BCI-AO game on power of mu band attenuation in stroke patients. Nineteen patients with subacute stroke were recruited. A BCI-AO game provided real-time feedback to participants regarding their attention to a flickering action video using steady-state visual-evoked potentials. All participants watched a video of repetitive grasping actions under two conditions: (1) BCI-AO game and (2) conventional AO, in random order. In the BCI-AO game, feedback on participants’ observation scores and observation time was provided. In conventional AO, a non-flickering video and no feedback were provided. The magnitude of mu suppression in the central motor, temporal, parietal, and occipital areas was significantly higher in the BCI-AO game than in the conventional AO. The magnitude of mu suppression was significantly higher in the BCI-AO game than in the conventional AO both in the affected and unaffected hemispheres. These results support the facilitatory effects of the BCI-AO game on mu suppression over conventional AO
Goethem, Sander Van; Adema, Kimberly; van Bergen, Britt; Viaene, Emilia; Wenborn, Eva; Verwulgen, Stijn
International Conference on Applied Human Factors and Ergonomics, Springer 2019.
Spatial awareness and the ability to analyze spatial objects, manipulate them and assess the effect thereof, is a key competence for industrial designers. Skills are gradually built up throughout most educational design programs, starting with exercises on technical drawings and reconstruction or classification of spatial objects from isometric projections and CAD practice. The accuracy in which spatial assignments are conducted and the amount of effort required to fulfill them, highly depend on individual insight, interests and persistence. Thus each individual has its own struggles and learning curve to master the structure of spatial objects in aesthetic and functional design. Virtual reality (VR) is a promising tool to expose subjects to objects with complex spatial structure, and even manipulate and design spatial characteristics of such objects. The advantage of displaying spatial objects in VR, compared to representations by projecting them on a screen or paper, could be that subjects could more accurately assess spatial properties of and object and its full geometrical and/or mechanical complexity, when exposed to that object in VR. Immersive experience of spatial objects, could not only result in faster acquiring spatial insights, but also potentially with less effort. We propose that acquiring spatial insight in VR could leverage individual differences in skills and talents and that under this proposition VR can be used as a promising tool in design education. A first step in underpinning this hypothesis, is acquisition of cognitive workload that can be used and compared both in VR and in a classical teaching context. We use electroencephalography (EEG) to assess brain activity through wearable plug and play headset (Wearable Sensing-DSI 7). This equipment is combined with VR (Oculus). We use QStates classification software to compare brain waves when conducting spatial assessments on paper and in VR. This gives us a measure of cognitive workload, as a ratio of a resulting from subject records with a presumed ‘high’ workload. A total number of eight records of subjects were suited for comparison. No significant difference was found between EEG signals (paried t-test, p = 0.57). However the assessment of cognitive workload was successfully validated through a questionnaire. The method could be used to set up reliable constructs for learning techniques for spatial insights.
Rice, Merrill G; Snider, Dallas; Drollinger, Sabrina; Greil, Chris; Bogni, Frank; Phillips, Jeffrey; Raj, Anil; Marco, Katherine; Linnville, Steven
In: Aerospace Medicine and Human Performance, vol. 90, no. 4, pp. 369–377, 2019.
INTRODUCTION: Prior research suggests there may be gender differences with regards to hypoxia resilience. Our study was designed to determine whether there were differences between genders in neuronal electrical activity at simulated altitude and whether those changes correlated with cognitive and aviation performance decrements.
METHODS: There were 60 student Naval Aviators or Flight Officers who completed this study (30 women, 30 men). Participants were exposed to increasing levels of normobaric hypoxia and monitored with dry EEG while flying a fixed-base flight simulation. Gender differences in brainwave frequency power were quantified using MATLAB. Changes in flight and cognitive performance were analyzed via simulation tasks and with a cognitive test validated under hypoxia.
RESULTS: Significant decreases in theta and gamma frequency power occurred for women compared to men with insidious hypoxic exposures to 20K, with an average frequency power decrease for women of 19.4% compared to 9.3% for men in theta, and a 42.2% decrease in gamma for women compared to 21.7% for men. Beta frequency power correlated highest between genders, with an average correlation coefficient of r = 0.95 across seven channels.
DISCUSSION: Results of this study suggest there is identifiable brain wave suppression for both men and women with hypoxic exposure and, moreover, there are significant differences in this suppression between genders. Beta frequency power was most sensitive for both genders and highly correlative compared to other brainwave frequencies. The implications of these findings are important considerations for next-generation aviation helmets, which may employ this technology as an early warning mechanism.
University of New England 2019, visited: 21.03.2019.
A team at the University of New England is moving closer - literally - to solving the mystery of how Parkinson's disease progresses, and rural Australians will soon play their part.
Flumeri, Gianluca Di; Aric`o, Pietro; Borghini, Gianluca; Sciaraffa, Nicolina; Florio, Antonello Di; Babiloni, Fabio
In: Sensors, vol. 19, no. 6, pp. 1365, 2019.
One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort.
Rice, Merrill G; Snider, Dallas; Drollinger, Sabrina; Greil, Chris; Bogni, Frank; Phillips, Jeffrey; Raj, Anil; Marco, Katherine; Linnville, Steven
In: Aerospace Medicine and Human Performance, vol. 90, no. 2, pp. 92-100, 2019.
INTRODUCTION: Recently, portable dry electroencephalographs (dry-EEGs) have indexed cognitive workload, fatigue, and drowsiness in operational environments. Using this technology this project assessed whether significant changes in brainwave frequency power occurred in response to hypoxic exposures as experienced in military aviation.
METHODS: There were 60 (30 women, 30 men) student Naval Aviators or Flight Officers who were exposed to an intense (acute) high-altitude (25,000 ft) normobaric hypoxic exposure, and 20 min later, more gradual (insidious) normobaric hypoxic exposure up to 20,000 ft while flying a fixed-wing flight simulation and monitored with a dry-EEG system. Using MATLAB, EEG frequencies and power were quantified and analyzed. Cognitive performance was also assessed with a cognitive task validated under hypoxia. Normobaric hypoxia and O2 saturation (Spo 2) were produced and monitored using the Reduced Oxygen Breathing Device (ROBD2).
RESULTS: Significant Spo 2 decreases were recorded at acute 25K and insidious 20K simulated altitudes. Significant power decreases were recorded in all frequencies (alpha, beta, gamma, and theta) and all channels with acute 25K exposures. Gamma, beta, and theta frequency power were significantly decreased with insidious 20K exposures at most of the channels. The frequency power decreases corresponded to significant decreases in cognitive performance and flight performance. Most importantly, frequency power suppressions occurred despite 42% of the volunteers not perceiving they were hypoxic in the acute phase, nor 20% in the insidious phase.
DISCUSSION: Results suggest EEG suppression during acute/insidious hypoxia can index performance decrements. These findings have promising implications in the development of biosensors that mitigate potential in-flight hypoxic physiological episodes.
Lim, Hyunmi; Ku, Jeonghun
In: Journal of Neuroscience Methods, vol. 314, pp. 21-27, 2019.
The number of commands in a brain–computer interface (BCI) system is important. This study proposes a new BCI technique to increase the number of commands in a single BCI system without loss of accuracy.
We expected that a flickering action video with left and right elbow movements could simultaneously activate the different pattern of event-related desynchronization (ERD) according to the video contents (e.g., left or right) and steady-state visually evoked potential (SSVEP). The classification accuracy to discriminate left, right, and rest states was compared under the three following feature combinations: SSVEP power (19–21 Hz), Mu power (8–13 Hz), and simultaneous SSVEP and Mu power.
The SSVEP feature could discriminate the stimulus condition, regardless of left or right, from the rest condition, while the Mu feature discriminated left or right, but was relatively poor in discriminating stimulus from rest. However, combining the SSVEP and Mu features, which were evoked by the stimulus with a single frequency, showed superior performance for discriminating all the stimuli among rest, left, or right.
Comparison with the existing method
The video contents could activate the ERD differently, and the flickering component increased its accuracy, such that it revealed a better performance to discriminate when considering together.
This paradigm showed possibility of increasing performance in terms of accuracy and number of commands with a single frequency by applying flickering action video paradigm and applicability to rehabilitation systems used by patients to facilitate their mirror neuron systems while training.
Arakaki, Xianghong; Lee, Ryan; King, Kevin S; Fonteh, Alfred N; Harrington, Michael G
In: PloS one, vol. 14, no. 1, pp. e0208517, 2019.
Our aim is to explore if cognitive challenge combined with objective physiology can reveal abnormal frontal alpha event-related desynchronization (ERD), in early Alzheimer’s disease (AD). We used quantitative electroencephalography (qEEG) to investigate brain activities during N-back working memory (WM) processing at two different load conditions (N = 0 or 2) in an aging cohort. We studied 60–100 year old participants, with normal cognition, and who fits one of two subgroups from cerebrospinal fluid (CSF) proteins: cognitively healthy (CH) with normal amyloid/tau ratio (CH-NAT, n = 10) or pathological amyloid/tau ratio (CH-PAT, n = 14). We recorded behavioral performances, and analyzed alpha power and alpha spectral entropy (SE) at three occasions: during the resting state, and at event-related desynchronization (ERD) [250 ~ 750 ms] during 0-back and 2-back. During 0-back WM testing, the behavioral performance was similar between the two groups, however, qEEG notably differentiated CH-PATs from CH-NATs on the simple, 0-back testing: Alpha ERD decreased from baseline only in the parietal region in CH-NATs, while it decreased in all brain regions in CH-PATs. Alpha SE did not change in CH-NATs, but was increased from baseline in the CH-PATs in frontal and left lateral regions (p<0.01), and was higher in the frontal region (p<0.01) of CH-PATs compared to CH-NATs. The alpha ERD and SE analyses suggest there is frontal lobe dysfunction during WM processing in the CH-PAT stage. Additional power and correlations with behavioral performance were also explored. This study provide pilot information to further evaluate whether this biomarker has clinical significance.
Pereira, Arnaldo; Padden, Dereck; Jantz, Jay; Lin, Kate; Alcaide-Aguirre, Ramses
EEG event-related potentials, and the P300 signal in
particular, are promising modalities for brain-computer interfaces (BCI). But the nonstationarity of EEG signals and
their differences across individuals have made it difficult to
implement classifiers that can determine user intent without having to be retrained or calibrated for each new user
and sometimes even each session. This is a major impediment to the development of consumer BCI. Recently, the
EEG BCI literature has begun to apply convolutional neural
networks (CNNs) for classification, but experiments have
largely been limited to training and testing on single subjects. In this paper, we report a study in which EEG data
were recorded from 66 subjects in a visual oddball task
in virtual reality. Using wide residual networks (WideResNets), we obtain state-of-the-art performance on a test set
composed of data from all 66 subjects together. Additionally, a minimal preprocessing stream to convert EEG data
into square images for CNN input while adding regularization is presented and shown to be viable. This study also
provides some guidance on network architecture parameters based on experiments with different models. Our results show that it may possible with enough data to train
a classifier for EEG-based BCIs that can generalize across
individuals without the need for individual training or calibration.
Camp, Marieke Van; Boeck, Muriel De; Verwulgen, Stijn; Bruyne, Guido De
International Conference on Applied Human Factors and Ergonomics, vol. 775, Springer Advances in Intelligent Systems and Computing , 2018.
Along with a growing interest in experience-driven design, interest in measuring user experience has progressively increased. This study explores the use of EEG for empirical UX evaluation. A first experimental test was conducted to measure and understand the effect of sensory stimuli on the user experience. A first experimental test was carried out with eight participants. A series of videos, eliciting positive and negative emotional responses, were presented to the participants. Subsequently, auditory stimuli were introduced and the effect on the user experience was evaluated using EEG measurements techniques and analysis software. After the tests the participants were questioned to verify whether the subjective results matched the objective measurements.
Raj, Anil; Roberts, Brooke; Hollingshead, Kristy; McDonald, Neil; Poquette, Melissa; Soussou, Walid
International Conference on Augmented Cognition, Springer 2018.
Tags: Cognitive-Algorithm| |
While operators performing tasks with high workload can increase task performance in response to limited increases in cognitive stress, chronic or rapidly accelerating stress can exceed the operator’s ability to compensate, generating acute cognitive strain (ACS). ACS represents a state wherein performance, situation awareness and cooperativity deteriorate markedly, leading to critical errors, mishaps or casualties. Nearly two decades of augmented cognition (AugCog) research has demonstrated the utility of psychophysiologic sensing and analysis for identification and tracking of changes in cognitive state and to modulate human machine interactions for improving system task performance. The proposed approach leveraged prior efforts to modulate cognitive stress using a multiagent approach to acquire and analyze multiple Psychophysiologic sensory channels, including changes in vocalizations, to create a reliable and non-intrusive Detector of Acute Cognitive Strain (DACS). The DACS system provides an integrated wearable multi-modal Research Sensor Suite (RSS) using the open-source Adaptive Multiagent Integration (AMI) architecture, that includes analysis agents for electroencephalograph (EEG), electromyography (EMG), video oculography (VOG), vocalization, and others to identify and correlate physiological signatures with cognitive stress and strain. An online AMI agent-based processing algorithm was developed and applied to audio communications to evaluate for changes in speaker vocalization fundamental frequency (F0) and cadence (utterances per minute). This paper describes initial phase results of aerospace mishap vocalization stress marker detection, a potential element of the proposed DACS system. DACS could use these markers to trigger adaptive automation agents that reduce task load and allow pilots to prevent or recover from ACS episodes.
Vergeer, Mark; Mesik, Juraj; Baek, Yihwa; Wilmerding, Kelton; Engel, Stephen A
Orientation-selective contrast adaptation measured with SSVEP Journal Article
In: Journal of Vision, vol. 18, no. 5, pp. 2–2, 2018.
Exposure to oriented luminance contrast patterns causes a reduction in visual sensitivity specifically for the adapter orientation. This orientation selectivity is probably the most studied aspect of contrast adaptation, but it has rarely been measured with steady-state visually evoked potentials (SSVEPs), despite their becoming one of the more popular methods of human neuroscience. Here, we measured orientation selective adaptation by presenting a plaid stimulus of which the horizontal and vertical grating reversed contrast at different temporal frequencies, while recording EEG signals from occipital visual areas. In three experiments, we compared SSVEP responses to the plaid before and after adaptation. All experiments showed a significant decrease in SSVEP response at the frequency of the adapter orientation, whereas such an effect was absent for the frequency of the orthogonal orientation. Adaptation also led to robust phase delays, selectively for the SSVEP frequency corresponding to the adapter orientation. These results demonstrate the efficiency of SSVEPs for measuring orientation selective adaptation; the method can measure changes in both amplitude and phase, simultaneously for two orientations.
Memmott, Tab; Eddy, Brandon; Dabiri, Sina; Erdogmus, Deniz; Fried-Oken, Melanie; Oken, Barry
In: Clinical Neurophysiology, vol. 129, pp. e61–e62, 2018.
Brain computer interfaces (BCI) generally require the user to maintain an attentive state. Potential end-users with severe speech and physical impairments may have limited communication abilities to report their current state, thus an automatic calculation of state may improve performance. It’s not yet known if an effective automatic calculation of drowsiness can be detected reliably in end-user populations or healthy controls.
Arakaki, Xianghong; Shoga, Michael; Li, Lianyang; Zouridakis, George; Tran, Thao; Fonteh, Alfred N; Dawlaty, Jessica; Goldweber, Robert; Pogoda, Janice M; Harrington, Michael G
In: PloS one, vol. 13, no. 2, pp. e0188101, 2018.
Diagnosing and monitoring recovery of patients with mild traumatic brain injury (mTBI) is challenging because of the lack of objective, quantitative measures. Diagnosis is based on description of injuries often not witnessed, subtle neurocognitive symptoms, and neuropsychological testing. Since working memory (WM) is at the center of cognitive functions impaired in mTBI, this study was designed to define objective quantitative electroencephalographic (qEEG) measures of WM processing that may correlate with cognitive changes associated with acute mTBI. First-time mTBI patients and mild peripheral (limb) trauma controls without head injury were recruited from the emergency department. WM was assessed by a continuous performance task (N-back). EEG recordings were obtained during N-back testing on three occasions: within five days, two weeks, and one month after injury. Compared with controls, mTBI patients showed abnormal induced and evoked alpha activity including event-related desynchronization (ERD) and synchronization (ERS). For induced alpha power, TBI patients had excessive frontal ERD on their first and third visit. For evoked alpha, mTBI patients had lower parietal ERD/ERS at the second and third visits. These exploratory qEEG findings offer new and non-invasive candidate measures to characterize the evolution of injury over the first month, with potential to provide much-needed objective measures of brain dysfunction to diagnose and monitor the consequences of mTBI.
Hunter, Aimee M; Nghiem, Thien X; Cook, Ian A; Krantz, David E; Minzenberg, Michael J; Leuchter, Andrew F
In: Clinical EEG and Neuroscience, vol. 49, no. 5, pp. 306–315, 2017.
Repetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in major depressive disorder (MDD), although clinical outcome is variable. Change in the resting-state quantitative electroencephalogram (qEEG), particularly in theta cordance early in the course of treatment, has been linked to antidepressant medication outcomes but has not been examined extensively in clinical rTMS. This study examined change in theta cordance over the first week of clinical rTMS and sought to identify a biomarker that would predict outcome at the end of 6 weeks of treatment. Clinically stable outpatients (n = 18) received nonblinded rTMS treatment administered to the dorsolateral prefrontal cortex (DLPFC). Treatment parameters (site, intensity, number of pulses) were adjusted on an ongoing basis guided by changes in symptom severity rating scale scores. qEEGs were recorded at pretreatment baseline and after 1 week of left DLPFC (L-DLPFC) rTMS using a 21-channel dry-electrode headset. Analyses examined the association between week 1 regional changes in theta band (4-8 Hz) cordance, and week 6 patient- and physician-rated outcomes. Theta cordance change in the central brain region predicted percent change in Inventory of Depressive Symptomology–Self-Report (IDS-SR) score, and improvement versus nonimprovement on the Clinical Global Impression–Improvement Inventory (CGI-I) (R2 = .38, P = .007; and Nagelkerke R2 = .78, P = .0001, respectively). The cordance biomarker remained significant when controlling for age, gender, and baseline severity. Treatment-emergent change in EEG theta cordance in the first week of rTMS may predict acute (6-week) treatment outcome in MDD. This oscillatory synchrony biomarker merits further study in independent samples.
Mills, Caitlin; Fridman, Igor; Soussou, Walid; Waghray, Disha; Olney, Andrew M; D'Mello, Sidney K
Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017.
Current learning technologies have no direct way to assess students' mental effort: are they in deep thought, struggling to overcome an impasse, or are they zoned out? To address this challenge, we propose the use of EEG-based cognitive load detectors during learning. Despite its potential, EEG has not yet been utilized as a way to optimize instructional strategies. We take an initial step towards this goal by assessing how experimentally manipulated (easy and difficult) sections of an intelligent tutoring system (ITS) influenced EEG-based estimates of students' cognitive load. We found a main effect of task difficulty on EEG-based cognitive load estimates, which were also correlated with learning performance. Our results show that EEG can be a viable source of data to model learners' mental states across a 90-minute session.
Halford, Jonathan J; Schalkoff, Robert J; Satterfield, Kevin E; Martz, Gabriel U; Kutluay, Ekrem; Waters, Chad G; Dean, Brian C
In: Journal of Clinical Neurophysiology, vol. 33, no. 6, pp. 530–537, 2016.
This purpose of this study was to evaluate the usefulness of a prototype battery-powered dry electrode system (DES) EEG recording headset in Veteran patients by comparing it with standard EEG.
Twenty-one Veterans had both a standard electrode system recording and DES recording in nine different patient states at the same encounter. Setup time, patient comfort, and subject preference were measured. Three experts performed technical quality rating of each EEG recording in a blinded fashion using the web-based EEGnet system. Power spectra were compared between DES and standard electrode system recordings.
The average time for DES setup was 5.7 minutes versus 21.1 minutes for standard electrode system. Subjects reported that the DES was more comfortable during setup. Most subjects (15 of 21) preferred the DES. On a five-point scale (1—best quality to 5—worst quality), the technical quality of the standard electrode system recordings was significantly better than for the DES recordings, at 1.25 versus 2.41 (P < 0.0001). But experts found that 87% of the DES EEG segments were of sufficient technical quality to be interpretable.
This DES offers quick and easy setup and is well tolerated by subjects. Although the technical quality of DES recordings was less than standard EEG, most of the DES recordings were rated as interpretable by experts.
This DES, if improved, could be useful for a telemedicine approach to outpatient routine EEG recording within the Veterans Administration or other health system.
Fridman, Igor; Cordeiro, Malaika; Rais-Bahrami, Khodayar; McDonald, Neil J; Jr, James J Reese; Massaro, An N; Conry, Joan A; Chang, Taeun; Soussou, Walid; Tsuchida, Tammy N
Evaluation of dry sensors for neonatal EEG recordings Journal Article
In: Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society, vol. 33, no. 2, pp. 149, 2016.
Neonatal seizures are a common neurologic diagnosis in Neonatal Intensive Care Units (NICUs), occurring in approximately 14,000 newborns annually in the US. While the only reliable means of detecting and treating neonatal seizures is with an EEG recording, many neonates do not get an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include: 1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, 2) poor signal quality due to improper skin preparation and artifact, 3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not been previously evaluated on neonates.
Sequential and simultaneous recordings with wet and dry sensors were performed for one hour on 27 neonates from 35-42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude, and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined.
Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8) given the non-superimposed sensor positions, and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically-relevant EEG background and seizure patterns. There was no skin injury after 1 hr of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix.
Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury.
Arakaki, Xianghong; Shoga, Michael; Li, Lianyang; Zouridakis, George; Rostami, Ramona; Goldweber, Robert; Harrington, Michael
Exploring neuroplasticity in acute mild traumatic brain injury Journal Article
In: The FASEB Journal, vol. 30, pp. 992–4, 2016.
To explore neuroplasticity in a longitudinal study of acute mild traumatic brain injury (mTBI).
We are using quantitative electroencephalography (qEEG) and magnetoencephalography (MEG) during the resting state and during cognitive brain stress to explore neuroplasticity in an ongoing acute mild traumatic brain injury research. Acute mTBI patients are recruited from the emergency department of Huntington Memorial Hospital in Pasadena, CA, and controls are non‐head‐trauma patients. Brain stress includes the N‐back (0‐back and 2‐back) working memory test and Color‐Word Interference Test (CWIT), administered using E‐prime software. Data were collected at three time points: within 1 week of injury, 14 days, and 30 days after injury. Behavioral as well as MEG and qEEG analysis are performed to compare the two groups.
Resting MEG detected low frequency activity in the mTBI group, consistent with previous publications. N‐back, in particular during 2‐back trials, and CWIT, in particular during incongruent trials, both show initial executive function impairment that improved on later visits. Time frequency analysis suggested corresponding compromised brain activity.
The EEG/MEG recordings during rest and brain stress are objective and sensitive to neuroplasticity in acute mTBI, and could be potential objective mTBI markers.
Kang, Dayoon; Kim, Jinsoo; Jang, Dong-Pyo; Cho, Yang Seok; Kim, Sung-Phil
In: Brain-Computer Interfaces, vol. 2, no. 4, pp. 193–201, 2015.
Brain-computer interfaces (BCIs) have been focused on providing direct communications to the disabled. Recently, BCI researchers have expanded BCI applications to non-medical uses and categorized them as active BCI, reactive BCI, and passive BCI. Neurocinematics, a new application of reactive BCIs, aims to understand viewers’ cognitive and affective responses to movies from neural activity, providing more objective information than traditional subjective self-reports. However, studies on analytical indices for neurocinematics have verified their indices by comparisons with self-reports. To overcome this contradictory issue, we proposed using an independent psychophysical index to evaluate a neural engagement index (NEI). We made use of the secondary task reaction time (STRT), which measures participants’ engagement in a primary task by their reaction time to a secondary task; here, responding to a tactile stimulus was the secondary task and watching a movie trailer was the primary task. NEI was developed as changes in the difference between frontal beta and alpha activity of EEG. We evaluated movie trailers using NEI, STRT, and self-reports and found a significant correlation between STRT and NEI across trailers but no correlation between any of the self-report results and STRT or NEI. Our results suggest that NEI developed for neurocinematics may conform well with more objective psychophysical assessments but not with subjective self-reports.
Li, Lianyang; Pagnotta, Mattia F; Arakaki, Xianghong; Tran, Thao; Strickland, David; Harrington, Michael; Zouridakis, George
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE IEEE, Milan, Italy, 2015, ISSN: 1558-4615.
In this study, we compared the brain activation profiles obtained from resting state Electroencephalographic (EEG) and Magnetoencephalographic (MEG) activity in six mild traumatic brain injury (mTBI) patients and five orthopedic controls, using power spectral density (PSD) analysis. We first estimated intracranial dipolar EEG/MEG sources on a dense grid on the cortical surface and then projected these sources on a standardized atlas with 68 regions of interest (ROIs). Averaging the PSD values of all sources in each ROI across all control subjects resulted in a normative database that was used to convert the PSD values of mTBI patients into z-scores in eight distinct frequency bands. We found that mTBI patients exhibited statistically significant overactivation in the delta, theta, and low alpha bands. Additionally, the MEG modality seemed to better characterize the group of individual subjects. These findings suggest that resting-state EEG/MEG activation maps may be used as specific biomarkers that can help with the diagnosis of and assess the efficacy of intervention in mTBI patients.
Kohli, Siddharth; Casson, Alexander J
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE Milan, Italy, 2015, ISSN: 1558-4615.
Conventional EEG (electroencephalography) has relied on wet electrodes which require conductive gel to help the electrodes make contact with the scalp. In recent years many dry electrode EEG systems have become available that do not require this gel. As a result they are quicker and easier to set up, with the potential to record the the EEG in situations and environments where it has not previously been possible. This paper investigates the practicality of using dry EEG in new non-conventional recording situations. In particular it uses a dry EEG recording system to monitor the EEG while a subject is riding an exercise bike. The results show that good-quality EEG, free from high-amplitude motion artefacts, can be collected in this challenging motion rich environment. In the frequency domain a peak of activity is seen over the motor cortex (C4) at 23 Hz starting five minutes after the start of the exercise task, giving initial insights into the on-going operation of the brain during exercise
Hairston, David W; Whitaker, Keith W; Ries, Anthony J; Vettel, Jean M; Bradford, Cortney J; Kerick, Scott E; McDowell, Kaleb
Usability of four commercially-oriented EEG systems Journal Article
In: Journal of Neural Engineering, vol. 11, no. 4, pp. 046018, 2014.
Electroencephalography (EEG) holds promise as a neuroimaging technology that can be used to understand how the human brain functions in real-world, operational settings while individuals move freely in perceptually-rich environments. In recent years, several EEG systems have been developed that aim to increase the usability of the neuroimaging technology in real-world settings. Here, the usability of three wireless EEG systems from different companies are compared to a conventional wired EEG system, BioSemi's ActiveTwo, which serves as an established laboratory-grade 'gold standard' baseline. The wireless systems compared include Advanced Brain Monitoring's B-Alert X10, Emotiv Systems' EPOC and the 2009 version of QUASAR's Dry Sensor Interface 10–20. The design of each wireless system is discussed in relation to its impact on the system's usability as a potential real-world neuroimaging system. Evaluations are based on having participants complete a series of cognitive tasks while wearing each of the EEG acquisition systems. This report focuses on the system design, usability factors and participant comfort issues that arise during the experimental sessions. In particular, the EEG systems are assessed on five design elements: adaptability of the system for differing head sizes, subject comfort and preference, variance in scalp locations for the recording electrodes, stability of the electrical connection between the scalp and electrode, and timing integration between the EEG system, the stimulus presentation computer and other external events.
McDonald, Neil J; Anumula, Harini A; Duff, Eric; Soussou, Walid
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2012, ISSN: 1557-170X.
Tags: Multimodal| |
This paper describes measurements made using an ECG system with QUASAR's capacitive bioelectrodes integrated into a pad system that is placed over a chair. QUASAR's capacitive bioelectrode has the property of measuring bioelectric potentials at a small separation from the body. This enables the measurement of ECG signals through fabric, without the removal of clothing or preparation of skin. The ECG was measured through the subject's clothing while the subject sat in the chair without any supporting action from the subject. The ECG pad system is an example of a high compliance system that places minimal requirements upon the subject and, consequently, can be used to generate a long-term record from ECG segments collected on a daily basis, providing valuable information on long-term trends in cardiac health.
Soussou, Walid; Rooksby, Michael; Forty, Charles; Weatherhead, James; Marshall, Sandra
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2012, ISSN: 1557-170X.
This paper describes a project whose goal was to establish the feasibility of using unobtrusive cognitive assessment methodologies in order to optimize efficiency and expediency of training. QUASAR, EyeTracking, Inc. (ETI), and Safe Passage International (SPI), teamed to demonstrate correlation between EEG and eye-tracking based cognitive workload, performance assessment and subject expertise on XRay screening tasks. Results indicate significant correlation between cognitive workload metrics based on EEG and eye-tracking measurements recorded during a simulated baggage screening task and subject expertise and error rates in that same task. These results suggest that cognitive monitoring could be useful in improving training efficiency by enabling training paradigms that adapts to increasing expertise.
Slater, Jeremy D; Kalamangalam, Giridhar P; Hope, Omotola
In: Journal of Neuroscience Methods, vol. 208, no. 2, pp. 134–137, 2012.
Tags: Comparisons| |
This study examines the difference in application times for routine electroencephalography (EEG) utilizing traditional electrodes and a “dry electrode” headset. The primary outcome measure was the time to interpretable EEG (TIE). A secondary outcome measure of recording quality and interpretability was obtained from EEG sample review by two blinded clinical neurophysiologists. With EEG samples obtained from 10 subjects, the average TIE for the “dry electrode” system was 139 s, and for the conventional recording 873 s (p < 0.001). The results support the hypothesis that such a “dry electrode” system can be applied with more than an 80% reduction in the TIE while still obtaining interpretable EEG.
McDonald, Neil J; Soussou, Walid
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE IEEE, 2011, ISSN: 1557-170X.
Tags: Cognitive-Algorithm| |
The Cognitive State Assessment Competition 2011 was organized by the U.S. Air Force Research Laboratory (AFRL) to compare the performance of real-time cognitive state classification software. This paper presents results for QUASAR's data classification module, QStates, which is a software package for real-time (and off-line) analysis of physiologic data collected during cognitive-specific tasks. The classifier's methodology can be generalized to any particular cognitive state; QStates identifies the most salient features extracted from EEG signals recorded during different cognitive states or loads.
Estepp, Justin R; Monnin, Jason W; Christensen, James C; Wilson, Glenn F
In: vol. 54, no. 3, pp. 210–214, 2010.
Advances in state-of-the-art dry electrode technology have led to the development of a novel dry electrode system for electroencephalography (QUASAR, Inc.; San Diego, California, USA). While basic systems-level testing and comparison of this dry electrode system to conventional wet electrode systems has proved to be very favorable, very limited data has been collected that demonstrates the ability of QUASAR's dry electrode system to replicate results produced in more applied, dynamic testing environments that may be used for human factors applications. In this study, QUASAR's dry electrode headset was used in combination with traditional wet electrodes to determine the ability of the dry electrode system to accurately differentiate between varying levels of cognitive workload. Results show that the accuracy in cognitive workload assessment obtained with wet electrodes is comparable to that obtained with the dry electrodes.
Antonenko, Pavlo; Paas, Fred; Grabner, Roland; Gog, Tamara Van
Using Electroencephalography to Measure Cognitive Load Journal Article
In: Educational Psychology Review, vol. 22, no. 4, pp. 425–438, 2010.
Tags: DSI-24| |
Application of physiological methods, in particular electroencephalography (EEG), offers new and promising approaches to educational psychology research. EEG is identified as a physiological index that can serve as an online, continuous measure of cognitive load detecting subtle fluctuations in instantaneous load, which can help explain effects of instructional interventions when measures of overall cognitive load fail to reflect such differences in cognitive processing. This paper presents a review of seminal literature on the use of continuous EEG to measure cognitive load and describes two case studies on learning from hypertext and multimedia that employed EEG methodology to collect and analyze cognitive load data.
PEBL Technical Report Series 2010.
Estepp, Justin R; Christensen, James C; Monnin, Jason W; Davis, Iris M; Wilson, Glenn F
Validation of a Dry Electrode System for EEG Conference
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 53, no. 18, SAGE Publications Sage CA: Los Angeles, CA 2009.
Tags: Comparisons| |
Electroencephalography (EEG) has been used for over 80 years to monitor brain activity. The basic technology of using electrodes placed on the scalp with conductive gel or paste (“wet electrodes”) has not fundamentally changed in that time. An electrode system that does not require conductive gel and skin preparation represents a major advancement in this technology and could significantly increase the utility of such a system for many human factors applications. QUASAR, Inc. (San Diego, CA) has developed a prototype dry electrode system for EEG that may well deliver on the promises of dry electrode technology; before any such system could gain widespread acceptance, it is essential to directly compare their system with conventional wet electrodes. An independent validation of dry vs. wet electrodes was conducted; in general, the results confirm that the data collected by the new system is comparable to conventional wet technology.
Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M
In: The International Test and Evaluation Association, vol. 30, pp. 13–17, 2009.
Tags: Cognitive-Algorithm| |
Quantum Applied Science and Research is working closely with the Aberdeen Test Center to develop an integrated system to monitor warfighter physiology. This need has been recognized by two recent major programs: the Defense Advanced Research Projects Agency’s Augmented Cognition program and the U.S. Army’s Warfighter Physiological Status Monitor program. However, these programs were limited by inadequate development of fully deployable noninvasive sensors and in the number of physiological variables they could simultaneously measure. Warfighters need to rapidly perceive, comprehend, and translate combat information into action. To aid them, robust gauges have been developed for classification of cognitive workload, engagement, and fatigue, which simplify complex physiological data into onedimensional parameters that can be used to identify a subject’s cognitive state during the varied tasks carried out in a training environment. This article describes the two main hardware modules that form part of an integrated Physiological Sensor Suite (PSS): a Physiological Status Monitor (PSM) and a module for the measurement of electroencephalograms (EEGs). The PSS is based on revolutionary noninvasive bioelectric sensor technologies. No modification of the skin’s outer layer is required for the operation of this sensor technology, unlike conventional electrode technology that requires the use of conductive pastes or gels, often with abrasive skin preparation of the electrode site. The PSS was designed to be wearable and unobtrusive, with an emphasis on the capability of long-term monitoring of physiological signals. These factors are of considerable importance in operational settings where high end-user compliance is required. The PSM is a simple belt that is worn around the chest. The EEG system has already been incorporated into a soldier’s Kevlar helmet and tested successfully during combat training. Data are acquired using a miniature, ultralowpower, microprocessor-controlled multichannel data acquisition (DAQ) unit that transmits data wirelessly to a base station/data logger worn by the subject. The DAQ unit is worn on the body close to the measurement point, reducing the amount of cable clutter and minimizing the impact on subject mobility without introducing motion artifacts.
Sellers, Eric W; Turner, Peter; Sarnacki, William A; McManus, Tobin; Vaughan, Theresa M; Matthews, Robert
International Conference on Human-Computer Interaction, Springer 2009.
Tags: BCI| |
A brain-computer interface is a device that uses signals recorded from the brain to directly control a computer. In the last few years, P300-based brain-computer interfaces (BCIs) have proven an effective and reliable means of communication for people with severe motor disabilities such as amyotrophic lateral sclerosis (ALS). Despite this fact, relatively few individuals have benefited from currently available BCI technology. Independent BCI use requires easily acquired, good-quality electroencephalographic (EEG) signals maintained over long periods in less-than-ideal electrical environments. Conventional, wet-sensor, electrodes require careful application. Faulty or inadequate preparation, noisy environments, or gel evaporation can result in poor signal quality. Poor signal quality produces poor user performance, system downtime, and user and caregiver frustration. This study demonstrates that a hybrid dry electrode sensor array (HESA) performs as well as traditional wet electrodes and may help propel BCI technology to a widely accepted alternative mode of communication.
Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2008.
Tags: Cognitive-Algorithm| |
This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload
Matthews, Robert; McDonald, Neil J; Hervieux, Paul; Turner, Peter J; Steindorf, Martin A
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2007, ISSN: 1558-4615.
Tags: Comparisons| |
This paper describes an integrated Physiological Sensor Suite (PSS) based upon QUASAR's innovative noninvasive bioelectric sensor technologies that will provide, for the first time, a fully integrated, noninvasive methodology for physiological sensing. The PSS currently under development at QUASAR is a state-of-the-art multimodal array of that, along with an ultra-low power personal area wireless network, form a comprehensive body-worn system for real-time monitoring of subject physiology and cognitive status. Applications of the PSS extend from monitoring of military personnel to long-term monitoring of patients diagnosed with cardiac or neurological conditions. Results for side-by-side comparisons between QUASAR's biosensor technology and conventional wet electrodes are presented. The signal fidelity for bioelectric measurements using QUASAR's biosensors is comparable to that for wet electrodes.
Matthews, Robert; McDonald, Neil J; Anumula, Harini; Woodward, Jamison; Turner, Peter J; Steindorf, Martin A; Chang, Kaichun; Pendleton, Joseph M
International Conference on Foundations of Augmented Cognition, Springer 2007.
Tags: Comparisons| |
This paper describes a wireless multi-channel system for zero-prep electroencephalogram (EEG) measurements in operational settings. The EEG sensors are based upon a novel hybrid (capacitive/resistive) bioelectrode technology that requires no modification to the skin’s outer layer. High impedance techniques developed for QUASAR’s capacitive electrocardiogram (ECG) sensors minimize the sensor’s susceptibility to common-mode (CM) interference, and permit EEG measurements with electrode-subject impedances as large as 107 Ω. Results for a side-by-side comparison between the hybrid sensors and conventional wet electrodes for EEG measurements are presented. A high level of correlation between the two electrode technologies (>99 subjects seated) was observed. The electronics package for the EEG system is based upon a miniature, ultra-low power microprocessor-controlled data acquisition system and a miniaturized wireless transceiver that can operate in excess of 72 hours from two AAA batteries.
Trejo, Leonard J; McDonald, Neil J; Matthews, Robert; Allison, Brendan Z
In: Foundations of Augmented Cognition, vol. 2007, pp. 13–22, 2007.
Tags: Multimodal| |
We report the results of an experiment designed to construct and test a robust multimodal system for automatic classification of cognitive overload. With the assistance of The Scripps Research Institute, twenty-two experienced gamers performed a first-person shooter combat simulation while multiple biosignals and performance were recorded. Signals included EEG, EOG, EMG, ECG, accuracy, and reaction time measures. A kernel partial least squares or KPLS classifier was trained to distinguish subtle differences in EEG spectra within subjects as they pertained to passive viewing, low-difficulty, and high-difficulty simulations. The KPLS classifier was supported and enhanced by algorithms for preprocessing and normalization of EEG and other biosignals. Results indicated that for some subjects, robust classifiers discriminated passive viewing from active performance with accuracies in the range of 99% to 100% and that such models were stable over two test days, using 35-70 min. of training data from Day 1 and testing on data from Day 2. In addition, for some subjects, classifiers discriminated high-difficulty simulations from low-difficulty and passive simulations with stable accuracies of 80% or better across two test days, using 35-70 min. of training data from Day 1 and testing on data from Day 2. We also tested the effect of alcohol intoxication on simulation performance and classifier accuracy. Performance was slightly altered by drinking alcohol to a blood alcohol level of 0.06%, producing more aggressive behavior than with a placebo across subjects. The KPLS classifiers showed remarkable resilience to alcohol effects, considerably less than the effects of day of testing. Not all subjects had such impressive results. To address the lack of generality across subjects, we are currently performing a modified study design that includes provisions for three calibration tasks. These calibration tasks are intended to serve as brief, simple tasks that a user could easily perform at any time as needed to
recalibrate algorithms that relate physiology to cognitive state. The tasks include a) eyes-open and eyes-closed for general EEG calibration, b) a mental arithmetic task (divide by twos or sevens) for cognitive load classification, and c) passive viewing of the combat simulation task, for calibration of engagement/disengagement of mental resources. We aim to use the data from the calibration tasks to adapt classification algorithms for variations over time and for inclusion of multiple sensor and data modalities, such as electrocardiographic, electrooculographic, and electromyographic sensor data
Park, Chulsung; Chou, Pai H; Bai, Ying; Matthews, Robert; Hibbs, Andrew
2006 IEEE biomedical circuits and systems conference, IEEE 2006.
Tags: Multimodal| |
Wearable electrocardiograph (ECG) monitoring systems today use electrodes that require skin preparation in advance, and require pastes or gels to make electrical contact to the skin. Moreover, they are not suitable for subjects at high levels of activity due to high noise spikes that can appear in the data. To address these problems, a new class of miniature, ultra low noise, capacitive sensor that does not require direct contact to the skin, and has comparable performance to gold standard ECG electrodes, has been developed. This paper presents a description and evaluation of a wireless version of a system based on these innovative ECG sensors. We use a wearable and ultra low power wireless sensor node called Eco. Experimental results show that the wireless interface will add minimal size and weight to the system while providing reliable, untethered operation.
Matthews, Robert; McDonald, Neil J; Anumula, Harini; Trejo, Leonard J
vol. 2006, AugCog International Conference Citeseer, 2006.
Practical sensing of biopotentials such as EEG or ECG in operational settings has been severely limited by the need
for skin preparation and conductive electrolytes at the skin-sensor interface. Another seldom-noted problem has
been the need for a conductive connection from the body to ground for cancellation of common-mode noise voltages. At QUASAR, we have developed a novel hybrid (capacitive/conductive) sensor that requires no skin preparation or electrolytes. In addition we have developed a special common-mode follower that allows a dry electrode to
be used for the ground. The electronics for the sensors and common-mode follower have low power requirements
and are miniaturized to fit within a compact sensor case. We are extending our tests of the hybrid sensor in three
human-machine interaction contexts: 1) a real-time system of multimodal physiological gauges for improved human-automation reliability, 2) EEG-based cognitive-overload detection in an urban combat simulation, and 3) a
brain-computer interface for EEG-based communication in the severely disabled. In all three contexts we compared
the QUASAR hybrid sensors with traditional conductive electrodes for EEG or ECG recordings. We discuss the recording fidelity, noise characteristics, ease of use, and reliability of the hybrid sensors versus the conventional conductive electrodes in all contexts.
Wallerius, John; Trejo, Leonard J; Matthews, Robert; Rosipal, Roman; Caldwell, John A
We developed an algorithm to extract and combine EEG spectral features, which effectively classifies cognitive
states and is robust in the presence of sensor noise. The algorithm uses a partial-least squares (PLS) algorithm to
decompose multi-sensor EEG spectra into a small set of components. These components are chosen such that they
are linearly orthogonal to each other and maximize the covariance between the EEG input variables and discrete
output variables, such as different cognitive states. A second stage of the algorithm uses robust cross-validation
methods to select the optimal number of components for classification. The algorithm can process practically
unlimited input channels and spectral resolutions. No a priori information about the spatial or spectral distributions
of the sources is required. A final stage of the algorithm uses robust cross-validation methods to reduce the set of
electrodes to the minimum set that does not sacrifice classification accuracy. We tested the algorithm with simulated
EEG data in which mental fatigue was represented by increases frontal theta and occipital alpha band power. We
synthesized EEG from bilateral pairs of frontal theta sources and occipital alpha sources generated by second-order
autoregressive processes. We then excited the sources with white noise and mixed the source signals into a 19-
channel sensor array (10-20 system) with the three-sphere head model of the BESA Dipole Simulator. We generated
synthetic EEG for 60 2-second long epochs. Separate EEG series represented the alert and fatigued states, between
which alpha and theta amplitudes differed on average by a factor of two. We then corrupted the data with broadband
white noise to yield signal-to-noise ratios (SNR) between 10 dB and -15 dB. We used half of the segments for
training and cross-validation of the classifier and the other half for testing. Over this range of SNRs, classifier
performance degraded smoothly, with test proportions correct (TPC) of 94%, 95%, 96%, 97%, 84%, and 53% for
SNRs of 10 dB, 5 dB, 0 dB, -5 dB, -10 dB, and -15 dB, respectively. We will discuss the practical implications of
this algorithm for real-time state classification and an off-line application to EEG data taken from pilots who
performed cognitive and flight tests over a 37-hour period of extended wakefulness.
Matthews, Robert; McDonald, NJ; Fridman, I; Hervieux, P; Nielsen, T
In: Foundations of Augmented Cognition, pp. 221, 2005.
The principle technical difficulty in measuring bioelectric signals from the body, such as electroencephalogram
(EEG) and electrocardiogram (ECG), lies in establishing good, stable electrical contact to the skin. Traditionally,
measurements of human bioelectric activity use resistive contact electrodes, the most widely used of which are
‘paste-on’ (or wet) electrodes. However, the use of wet electrodes is a highly invasive process as some preparation
of the skin is necessary in order for the electrode either to adhere to the skin for any length of time or to make
adequate electrical contact to the skin. This is uncomfortable for the subject and can lead to considerable irritation of
the skin over time, an issue of particular concern in measurements of EEG signals, which typically require an array
of electrodes positioned about the head.
Despite over 40 years of investigation, including the development of several alternative electrode technologies, no
reliable method for making electrical contact to the skin that does not require some modification of its outer layer
has been developed. For example, Ag-AgCl dry electrodes, NASICON ceramic electrodes, and saline solution
electrodes do not require any skin preparation, but for each electrode the subject experiences skin irritation over
extended periods, and there are various issues that cause the performance of the sensors to degrade over time.
Alternatively, insulated electrodes that use capacitive coupling to measure the potential changes on the skin have in
the past, for noise considerations, used exotic materials to generate a high capacitive coupling (~1 nF) to the skin.
The intrinsic noise of insulated electrodes is adequate for bioelectric measurements, but these high capacitance
sensors also exhibit long-term compatibility issues with the skin and are sensitive to motion artifact signals due to
the electrode’s high sensitivity to relative motion between the skin and the electrode itself.
As a result of advances in semiconductor processing techniques and through the use of innovative circuit designs,
QUASAR has developed a new class of insulated bioelectrodes (IBEs) that can measure the electric potential at a
point in free space. This has made it possible to make measurements of human bioelectric signals without a resistive
connection and with modest capacitive coupling to the source of interest. These electrodes are genuinely noninvasive in that they require no skin preparation, have no long-term compatibility issues with the skin, and can
measure human bioelectric activity at the microvolt level through clothing while remaining largely immune to
motion artifact signals.
This paper will present measurements of bioelectric activity made using QUASAR’s IBEs, and corresponding data
measured using conventional wet electrodes will also be presented for comparison. The presentation will include
through-clothing measurements of bioelectric signals, the rejection of motion artifact signals, non-invasive (i.e. no
skin preparation) EEG measurements of alpha-rhythm signals, and the noise levels observed using both types of
In a series of tests conducted on unprepared skin, it was observed that both the IBEs and conventional wet electrodes
had similar noise levels. This noise level was higher than the expected noise level, which had been predicted based
upon the intrinsic noise characteristics of the sensors. The fact that both sensors suffered from this higher noise level
suggested a common mechanism, which was later identified as skin noise.
It has been reported in the literature that one of the fundamental noise sources for any bioelectric measurement made
on unprepared skin is epidermal artifact noise. This noise is due to potentials developed in the skin itself that are
indistinguishable from the bioelectric signal of interest. There exist techniques that can reduce this noise level by as
much as a factor of 5, but they involve modification of the skin’s outer layer either by abrasion or chemical absorption of conducting fluid. These methods are not comfortable for the subject and may be difficult to perform on
subjects with especially sensitive skin, such as neonates, burn victims, or the elderly.
In addition to QUASAR’s IBE sensors, this paper will also discuss a new free-space electrode that is designed to be
insensitive to epidermal artifact noise, and thus is capable of bioelectric measurements at the microvolt level in the
absence of any skin preparation. The new device exhibits significantly less capacitive coupling to the source of
interest than the current generation of QUASAR IBEs, without the increase in intrinsic sensor noise that would
accompany a reduction in electrode capacitance.
McCormick, DT; Tien, NC; MacDonald, N; Matthews, R; Hibbs, A
The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, 2005. Digest of Technical Papers. TRANSDUCERS'05., vol. 1, IEEE 2005.
Theoretical and experimental results of a design methodology and fabrication technology to realize ultrawide tuning range, electrostatic, silicon micromachined capacitors are presented. The varactors achieve a maximum tuning range of approximately 4000% and exhibit a linear tuning range of 1000% (C vs. V/sup 2/). The devices are also designed and characterized with Tera-ohm isolation and sub 30 fF capacitive coupling between the driving actuator and tuning element. In addition, parasitic capacitances have been minimized to less than 22 fF at the tuning element terminals.
Matthews, Robert; McDonald, Neil J; Trejo, Leonard J
Psycho-Physiological Sensor Techniques: An Overview Journal Article
In: Foundations of augmented cognition, vol. 11, pp. 263–272, 2005.
Under the auspices of the Defense Advanced Research Projects Agency (DARPA), the Augmented Cognition
(AugCog) programme is striving to realize the unambiguous determination of the cognitive state via physiological
measurements. The basis of this programme is the assumption that as computing power continues to increase, the
exchange of information between computers and their human users is fundamentally limited by human information
processing capabilities, particularly when the users are fatigued or placed under stressful conditions such as those
present in warfare command environments. Knowledge of the cognitive state will enable the development of a
human-computer interface that adapts to optimize the processing of information by the user.
At present there is no direct measure of a subject’s cognitive state. However, it is possible to use psycho-physiological
techniques to infer the cognitive state, in which changes in physiological signals that are affected by the cognitive
state (e.g. electroencephalographic signals, variations in the heart rate or blood flow in the brain) are measured using
a suite of bioelectric and biophysical sensors, and then processed using sophisticated algorithms based upon
theoretical descriptions of the relationship between the cognitive state and the relevant physiological signals.
This paper will provide an overview of current psycho-physiological related research, quoting recent results from
groups working in the AugCog programme and elsewhere. The latest advancements in sensor technologies will be
reviewed in an effort to identify those physiological sensors that are most useful in determining the cognitive state,
with particular attention paid to the quality of information provided by the sensor and design elements such as the
degree of comfort for the human test subject, sensor power requirements, ease of use, and cost. The compatibility of
each sensor type with other technologies will also be assessed and a psycho-physiological integrated sensor suite,
incorporating those sensors identified as being best suited to the determination of the cognitive state, will be proposed.
Trejo, Leonard J; Wheeler, Kevin R; Jorgensen, Charles C; Rosipal, Roman; Clanton, Sam T; Matthews, Bryan; Hibbs, Andrew D; Matthews, Robert; Krupka, Michael
Multimodal neuroelectric interface development Journal Article
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 199–203, 2003.
We are developing electromyographic and electroencephalographic methods, which draw control signals for human-computer interfaces from the human nervous system. We have made progress in four areas: 1) real-time pattern recognition algorithms for decoding sequences of forearm muscle activity associated with control gestures; 2) signal-processing strategies for computer interfaces using electroencephalogram (EEG) signals; 3) a flexible computation framework for neuroelectric interface research; and d) noncontact sensors, which measure electromyogram or EEG signals without resistive contact to the body.
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