2025 |
Wang, Zehua; Zhang, Ye; Ma, Ning; Qiao, Huiting; Xia, Meiyun; Li, Deyu Study on cognitive impairment evaluation based on photoelectric neural information Journal Article In: Journal of Alzheimer's Disease Reports, vol. 9, pp. 25424823251325537, 2025. Abstract | Links | Tags: Biomarker, DSI-7 @article{wang2025study,Background Whether there is a cognitive load-dependent brain activation pattern in the pre-Alzheimer's disease phase is unknown. Multimodal system provides a powerful technical tool. Objective We evaluated brain activity patterns under different cognitive loads in patients with mild cognitive impairment. Methods Functional near-infrared spectroscopy signals and electroencephalography signals were acquired from the mild cognitive impairment group (MCI, n = 20) and the healthy control group (HC, n = 24) under four cognitive loads. We analyzed the respective brain activity features and performed correlation analyses. Results (1) During the encoding phase, both the left occipital (pcond = 0.05, pgroup < 0.01) and left temporal (pcond = 0.02, pgroup = 0.03) skewness condition effects and between-group effects were significant. (2) As the cognitive load increased, the clustering coefficients and local efficiencies were significantly lower for the HC group. (3) The left occipital and left temporal activation skewness in the MCI group were significantly correlated with left occipital electrical features, whereas the left occipital activation intensity and skewness were significantly correlated with left occipital electrical features in HC group. Conclusions The pattern of brain activity in MCI depends on cognitive load. Left occipital and left temporal may be important brain regions for evaluating MCI and need to be focused on in the future. |
Peiro, Nicolas Calvo; Haugland, Mathias Ramm; Kutuzova, Alena; Graef, Cosima; Bocum, Aminata; Tai, Yen Foung; Borovykh, Anastasia; Haar, Shlomi Deep Learning-Driven EEG Analysis for Personalized Deep Brain Stimulation Programming in Parkinson's Disease Journal Article In: medRxiv, pp. 2025–02, 2025. Abstract | Links | Tags: Biomarker, DSI-7, Neuromodulation @article{calvo2025deep,Deep Brain Stimulation (DBS) is an invasive procedure used to alleviate motor symptoms in Parkinson’s Disease (PD) patients. While brain activity can be used to optimise DBS parameters, the impact of DBS parameters on brain activity remains unclear. We aimed to identify the cortical neural response to changes in DBS parameters, which are sensitive to the effect of small changes in the stimulation parameters and could be used as neural biomarkers. We recorded in-clinic EEG data from seven hemispheres of PD patients during DBS programming sessions. Here we developed a siamese adaptation of the EEGNet deep learning architecture and trained it to distinguish whether two short (1-sec-long) segments of brain activity were taken with the same stimulation parameters, or if either the strength or location of the stimulation had changed. 13 independent models were trained independently in each hemisphere for stimulation amplitude or contact, and all achieved high accuracy with an average of 78%. Our models are sensitive to changes in brain activity recorded at the scalp of the patients following changes as small as 0.3mA in the DBS parameters. Next, we interpreted what our black-box AI models learned with an ablation-based explainability method, that extracts frequency bands learned by the models through a perturbation of the input’s frequency spectrum. We found that fast Narrow-Band Gamma oscillations (60-90Hz), contributed most to the models across all 7 hemispheres. This work, using a data-driven approach, joins a recent body of evidence suggesting cortical Narrow-Band Gamma activity as the potential range for digital biomarkers for DBS optimization. |
2024 |
Jang, Dajeong; Kim, Han-Jong; Choi, Kyungah Enhancing Student Learning in Virtual Classrooms: Effects of Window View Content and Time of Day Journal Article In: IEEE Access, 2024. Abstract | Links | Tags: DSI-7, VR @article{jang2024enhancing,As virtual classrooms, traditional physical classroom environments are transformed into flexible virtual environments, allowing customization of environmental elements to enhance student learning. This study explored the effects of window settings in virtual classrooms on learning experiences of students. Utilizing a within-subjects design, we simulated a virtual classroom environment with seven unique window settings and varied its view content (nature vs. urban) and time of day (daytime, sunset, and night). We also simulated a windowless condition. Thirty-five university students participated in the study and performed subjective evaluations and cognitive tasks. Moreover, their physiological responses were recorded using electroencephalogram measurements. The results indicated that environments with windows increased the perception of spaciousness and promoted a state of relaxed alertness, as evidenced by increased fast alpha brainwave activity. In contrast, settings without windows or with urban views increased the sense of presence. Daytime views positively affected valence, motivation, spaciousness, and concentration, whereas nighttime views were the least preferred. No significant differences were observed in cognitive task performance across the different conditions. These findings underscore the necessity of customizing virtual learning environments to meet individual user needs. By allowing students to adjust their virtual environments, educators and space designers can create more flexible and personalized virtual-reality educational spaces, ultimately improving learning outcomes. |
2023 |
Kamti, Mukesh Kumar; Iqbal, Rauf; Kakoti, Pallabjyoti Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions Journal Article In: Transportation Research Part F: Traffic Psychology and Behaviour, vol. 99, pp. 98–112, 2023. Abstract | Links | Tags: Cognitive-Algorithm, DSI-7 @article{kamti2023eeg,Driving in diverse and challenging conditions, including inclement weather, poses potential risks to road safety. While previous studies have primarily focused on examining driver behavior and reactions in different weather and road conditions, there is a lack of research on assessing drivers' mental states during such situations, particularly considering the influence of factors such as road complexity, traffic, demographics, and adverse environmental conditions. This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels through simulated driving scenarios encompassing various environments and traffic conditions. A three-wheeler driving simulator was employed, along with the DSI 7 EEG headset and Q states software, to classify and analyze the drivers' mental states. The findings of this study highlight that young novice drivers exhibit higher fluctuations in mental state compared to their mid and high-experienced counterparts. Furthermore, mid-age drivers face an elevated risk of collision due to frequent changes in mental state and attention. Additionally, it was observed that highly skilled drivers display a transition in attention level and mental state between sessions, shifting from a focused to a relaxed state—an aspect absent in inexperienced drivers. These findings enhance our comprehension of the intricate interaction among drivers' emotional states, age, experience, and driving abilities, consequently opening avenues for tailored interventions and training initiatives focused on improving road safety. |
Haugland, Mathias Ramm; Borovykh, Anastasia; Tai, Yen; Haar, Shlomi 2023 Conference on Cognitive Computational Neuroscience , Cognitive Computational Neuroscience, 2023. Abstract | Links | Tags: DSI-7 @conference{hauglandexplainable,Deep brain stimulation (DBS) is an effective technique for treating motor symptoms in neurological conditions like Parkinson’s disease and dystonic and essential tremor (DT and ET). The DBS delivery could be improved if reliable biomarkers could be found. We propose a deep learning (DL) framework based on EEGNet to search for digital biomarkers in EEG recordings for discriminating neural response from changes in DBS parameters. Here we present a proof-of-concept by distinguishing left and right arm movement in raw EEG recorded during a DBS programming session of a DT patient. Based on the classification of 1s segments from six-channel EEG, we achieve an average accuracy of up to 93.8%. In addition, we propose a simple, yet effective model-agnostic filtering strategy for explaining the network’s performance, showing which frequency band features it mostly uses to classify the EEG. |
Swerdloff, Margaret M; Hargrove, Levi J Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task Journal Article In: 2023. Abstract | Links | Tags: DSI-7 @article{swerdloffevaluating,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. |
Browne, Connor Evaluating the Effectiveness of Preprocessing Methods on Motor Classification Scores in EEG Data Masters Thesis University of Washington, 2023. Abstract | Links | Tags: DSI-7 @mastersthesis{browne2023evaluating,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. |
Chiossi, Francesco; Ou, Changkun; Mayer, Sven In: 2023. Abstract | Links | Tags: DSI-7, Multimodal, VR @article{chiossi2023exploring,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. |
2022 |
Chanpornpakdi, Ingon; Noda, Motoi; Tanaka, Toshihisa; Harpaz, Yuval; Geva, Amir B Clustering of advertising images using electroencephalogram Conference Proceedings of 2022 APSIPA Annual Summit and Conference, 2022. Abstract | Links | Tags: DSI-7, Neuromarketing @conference{chanpornpakdiclustering,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. |
Dong, Xian; Wu, Yeyu; Tu, Zhijun; Cao, Bin; Li, Xianting; Yang, Zixu; Liu, Fei; Xing, Zheli Influence of ambient temperature on personnel thermal comfort and working efficiency under isolation condition of underground engineering Journal Article In: Energy and Buildings, pp. 112438, 2022. Abstract | Links | Tags: DSI-7 @article{dong2022influence,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. |
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 Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface Journal Article In: Brain-Computer Interfaces, vol. 9, no. 3, pp. 179–192, 2022. Abstract | Links | Tags: BCI, DSI-7, Stroke @article{humphries2022motor,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. |
Rustamov, Nabi; Humphries, Joseph; Carter, Alexandre; Leuthardt, Eric C Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke Journal Article In: Brain Communications, vol. 4, iss. 3, 2022. Abstract | Links | Tags: BCI, DSI-7, Stroke @article{rustamov2022thetab,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. |
2021 |
Snider, Dallas H; Linnville, Steven E; Phillips, Jeffrey B; Rice, Merrill G Predicting hypoxic hypoxia using machine learning and wearable sensors Journal Article In: Biomedical Signal Processing and Control, vol. 71, pp. 103110, 2021. Abstract | Links | Tags: Cognitive-Algorithm, DSI-7 @article{snider2022predicting,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. |
Swerdloff, Margaret M; Hargrove, Levi J 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE 2021, ISBN: 978-1-7281-4338-5. Abstract | Links | Tags: DSI-7 @conference{swerdloff2021identifying,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. |
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. Abstract | Links | Tags: DSI-7 @article{rahimi2021image,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. |
2020 |
Neilson, Brittany N; Phillips, Jeffrey B; Snider, Dallas H; Drollinger, Sabrina M; Linnville, Steven E; Mayes, Ryan S A Data-Driven Approach to Aid in Understanding Brainwave Activity During Hypoxia Conference 2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID), IEEE IEEE, Miramar Beach, FL, USA, 2020, ISBN: 978-1-7281-5890-7. Abstract | Links | Tags: Biomarker, Cognitive-Algorithm, DSI-7 @conference{neilson2020data,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. |
2019 |
Goethem, Sander Van; Adema, Kimberly; van Bergen, Britt; Viaene, Emilia; Wenborn, Eva; Verwulgen, Stijn A Test Setting to Compare Spatial Awareness on Paper and in Virtual Reality Using EEG Signals Conference International Conference on Applied Human Factors and Ergonomics, Springer 2019. Abstract | Links | Tags: BCI, Cognitive-Algorithm, DSI-7, VR @conference{van2019test,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 Gender Differences in Dry-EEG Manifestations During Acute and Insidious Normobaric Hypoxia Journal Article In: Aerospace Medicine and Human Performance, vol. 90, no. 4, pp. 369–377, 2019. Abstract | Links | Tags: Biomarker, DSI-7 @article{rice2019gender,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. |
Flumeri, Gianluca Di; Aric`o, Pietro; Borghini, Gianluca; Sciaraffa, Nicolina; Florio, Antonello Di; Babiloni, Fabio In: Sensors, vol. 19, no. 6, pp. 1365, 2019. Abstract | Links | Tags: Comparisons, DSI-7 @article{di2019dry,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 Dry-EEG Manifestations of Acute and Insidious Hypoxia During Simulated Flight Journal Article In: Aerospace Medicine and Human Performance, vol. 90, no. 2, pp. 92-100, 2019. Abstract | Links | Tags: Biomarker, DSI-7 @article{rice2019dry,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. |
2018 |
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. Abstract | Links | Tags: DSI-7, VR @article{vergeer2018orientation,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. |
2015 |
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. Abstract | Links | Tags: Comparisons, DSI-7 @conference{kohli2015towards,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 |
2025 |
Wang, Zehua; Zhang, Ye; Ma, Ning; Qiao, Huiting; Xia, Meiyun; Li, Deyu Study on cognitive impairment evaluation based on photoelectric neural information Journal Article In: Journal of Alzheimer's Disease Reports, vol. 9, pp. 25424823251325537, 2025. @article{wang2025study,Background Whether there is a cognitive load-dependent brain activation pattern in the pre-Alzheimer's disease phase is unknown. Multimodal system provides a powerful technical tool. Objective We evaluated brain activity patterns under different cognitive loads in patients with mild cognitive impairment. Methods Functional near-infrared spectroscopy signals and electroencephalography signals were acquired from the mild cognitive impairment group (MCI, n = 20) and the healthy control group (HC, n = 24) under four cognitive loads. We analyzed the respective brain activity features and performed correlation analyses. Results (1) During the encoding phase, both the left occipital (pcond = 0.05, pgroup < 0.01) and left temporal (pcond = 0.02, pgroup = 0.03) skewness condition effects and between-group effects were significant. (2) As the cognitive load increased, the clustering coefficients and local efficiencies were significantly lower for the HC group. (3) The left occipital and left temporal activation skewness in the MCI group were significantly correlated with left occipital electrical features, whereas the left occipital activation intensity and skewness were significantly correlated with left occipital electrical features in HC group. Conclusions The pattern of brain activity in MCI depends on cognitive load. Left occipital and left temporal may be important brain regions for evaluating MCI and need to be focused on in the future. |
Peiro, Nicolas Calvo; Haugland, Mathias Ramm; Kutuzova, Alena; Graef, Cosima; Bocum, Aminata; Tai, Yen Foung; Borovykh, Anastasia; Haar, Shlomi Deep Learning-Driven EEG Analysis for Personalized Deep Brain Stimulation Programming in Parkinson's Disease Journal Article In: medRxiv, pp. 2025–02, 2025. @article{calvo2025deep,Deep Brain Stimulation (DBS) is an invasive procedure used to alleviate motor symptoms in Parkinson’s Disease (PD) patients. While brain activity can be used to optimise DBS parameters, the impact of DBS parameters on brain activity remains unclear. We aimed to identify the cortical neural response to changes in DBS parameters, which are sensitive to the effect of small changes in the stimulation parameters and could be used as neural biomarkers. We recorded in-clinic EEG data from seven hemispheres of PD patients during DBS programming sessions. Here we developed a siamese adaptation of the EEGNet deep learning architecture and trained it to distinguish whether two short (1-sec-long) segments of brain activity were taken with the same stimulation parameters, or if either the strength or location of the stimulation had changed. 13 independent models were trained independently in each hemisphere for stimulation amplitude or contact, and all achieved high accuracy with an average of 78%. Our models are sensitive to changes in brain activity recorded at the scalp of the patients following changes as small as 0.3mA in the DBS parameters. Next, we interpreted what our black-box AI models learned with an ablation-based explainability method, that extracts frequency bands learned by the models through a perturbation of the input’s frequency spectrum. We found that fast Narrow-Band Gamma oscillations (60-90Hz), contributed most to the models across all 7 hemispheres. This work, using a data-driven approach, joins a recent body of evidence suggesting cortical Narrow-Band Gamma activity as the potential range for digital biomarkers for DBS optimization. |
2024 |
Jang, Dajeong; Kim, Han-Jong; Choi, Kyungah Enhancing Student Learning in Virtual Classrooms: Effects of Window View Content and Time of Day Journal Article In: IEEE Access, 2024. @article{jang2024enhancing,As virtual classrooms, traditional physical classroom environments are transformed into flexible virtual environments, allowing customization of environmental elements to enhance student learning. This study explored the effects of window settings in virtual classrooms on learning experiences of students. Utilizing a within-subjects design, we simulated a virtual classroom environment with seven unique window settings and varied its view content (nature vs. urban) and time of day (daytime, sunset, and night). We also simulated a windowless condition. Thirty-five university students participated in the study and performed subjective evaluations and cognitive tasks. Moreover, their physiological responses were recorded using electroencephalogram measurements. The results indicated that environments with windows increased the perception of spaciousness and promoted a state of relaxed alertness, as evidenced by increased fast alpha brainwave activity. In contrast, settings without windows or with urban views increased the sense of presence. Daytime views positively affected valence, motivation, spaciousness, and concentration, whereas nighttime views were the least preferred. No significant differences were observed in cognitive task performance across the different conditions. These findings underscore the necessity of customizing virtual learning environments to meet individual user needs. By allowing students to adjust their virtual environments, educators and space designers can create more flexible and personalized virtual-reality educational spaces, ultimately improving learning outcomes. |
2023 |
Kamti, Mukesh Kumar; Iqbal, Rauf; Kakoti, Pallabjyoti Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions Journal Article In: Transportation Research Part F: Traffic Psychology and Behaviour, vol. 99, pp. 98–112, 2023. @article{kamti2023eeg,Driving in diverse and challenging conditions, including inclement weather, poses potential risks to road safety. While previous studies have primarily focused on examining driver behavior and reactions in different weather and road conditions, there is a lack of research on assessing drivers' mental states during such situations, particularly considering the influence of factors such as road complexity, traffic, demographics, and adverse environmental conditions. This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels through simulated driving scenarios encompassing various environments and traffic conditions. A three-wheeler driving simulator was employed, along with the DSI 7 EEG headset and Q states software, to classify and analyze the drivers' mental states. The findings of this study highlight that young novice drivers exhibit higher fluctuations in mental state compared to their mid and high-experienced counterparts. Furthermore, mid-age drivers face an elevated risk of collision due to frequent changes in mental state and attention. Additionally, it was observed that highly skilled drivers display a transition in attention level and mental state between sessions, shifting from a focused to a relaxed state—an aspect absent in inexperienced drivers. These findings enhance our comprehension of the intricate interaction among drivers' emotional states, age, experience, and driving abilities, consequently opening avenues for tailored interventions and training initiatives focused on improving road safety. |
Haugland, Mathias Ramm; Borovykh, Anastasia; Tai, Yen; Haar, Shlomi 2023 Conference on Cognitive Computational Neuroscience , Cognitive Computational Neuroscience, 2023. @conference{hauglandexplainable,Deep brain stimulation (DBS) is an effective technique for treating motor symptoms in neurological conditions like Parkinson’s disease and dystonic and essential tremor (DT and ET). The DBS delivery could be improved if reliable biomarkers could be found. We propose a deep learning (DL) framework based on EEGNet to search for digital biomarkers in EEG recordings for discriminating neural response from changes in DBS parameters. Here we present a proof-of-concept by distinguishing left and right arm movement in raw EEG recorded during a DBS programming session of a DT patient. Based on the classification of 1s segments from six-channel EEG, we achieve an average accuracy of up to 93.8%. In addition, we propose a simple, yet effective model-agnostic filtering strategy for explaining the network’s performance, showing which frequency band features it mostly uses to classify the EEG. |
Swerdloff, Margaret M; Hargrove, Levi J Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task Journal Article In: 2023. @article{swerdloffevaluating,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. |
Browne, Connor Evaluating the Effectiveness of Preprocessing Methods on Motor Classification Scores in EEG Data Masters Thesis University of Washington, 2023. @mastersthesis{browne2023evaluating,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. |
Chiossi, Francesco; Ou, Changkun; Mayer, Sven In: 2023. @article{chiossi2023exploring,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. |
2022 |
Chanpornpakdi, Ingon; Noda, Motoi; Tanaka, Toshihisa; Harpaz, Yuval; Geva, Amir B Clustering of advertising images using electroencephalogram Conference Proceedings of 2022 APSIPA Annual Summit and Conference, 2022. @conference{chanpornpakdiclustering,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. |
Dong, Xian; Wu, Yeyu; Tu, Zhijun; Cao, Bin; Li, Xianting; Yang, Zixu; Liu, Fei; Xing, Zheli Influence of ambient temperature on personnel thermal comfort and working efficiency under isolation condition of underground engineering Journal Article In: Energy and Buildings, pp. 112438, 2022. @article{dong2022influence,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. |
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 Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface Journal Article In: Brain-Computer Interfaces, vol. 9, no. 3, pp. 179–192, 2022. @article{humphries2022motor,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. |
Rustamov, Nabi; Humphries, Joseph; Carter, Alexandre; Leuthardt, Eric C Theta-gamma coupling as a cortical biomarker of brain-computer interface mediated motor recovery in chronic stroke Journal Article In: Brain Communications, vol. 4, iss. 3, 2022. @article{rustamov2022thetab,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. |
2021 |
Snider, Dallas H; Linnville, Steven E; Phillips, Jeffrey B; Rice, Merrill G Predicting hypoxic hypoxia using machine learning and wearable sensors Journal Article In: Biomedical Signal Processing and Control, vol. 71, pp. 103110, 2021. @article{snider2022predicting,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. |
Swerdloff, Margaret M; Hargrove, Levi J 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE 2021, ISBN: 978-1-7281-4338-5. @conference{swerdloff2021identifying,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. |
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. @article{rahimi2021image,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. |
2020 |
Neilson, Brittany N; Phillips, Jeffrey B; Snider, Dallas H; Drollinger, Sabrina M; Linnville, Steven E; Mayes, Ryan S A Data-Driven Approach to Aid in Understanding Brainwave Activity During Hypoxia Conference 2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID), IEEE IEEE, Miramar Beach, FL, USA, 2020, ISBN: 978-1-7281-5890-7. @conference{neilson2020data,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. |
2019 |
Goethem, Sander Van; Adema, Kimberly; van Bergen, Britt; Viaene, Emilia; Wenborn, Eva; Verwulgen, Stijn A Test Setting to Compare Spatial Awareness on Paper and in Virtual Reality Using EEG Signals Conference International Conference on Applied Human Factors and Ergonomics, Springer 2019. @conference{van2019test,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 Gender Differences in Dry-EEG Manifestations During Acute and Insidious Normobaric Hypoxia Journal Article In: Aerospace Medicine and Human Performance, vol. 90, no. 4, pp. 369–377, 2019. @article{rice2019gender,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. |
Flumeri, Gianluca Di; Aric`o, Pietro; Borghini, Gianluca; Sciaraffa, Nicolina; Florio, Antonello Di; Babiloni, Fabio In: Sensors, vol. 19, no. 6, pp. 1365, 2019. @article{di2019dry,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 Dry-EEG Manifestations of Acute and Insidious Hypoxia During Simulated Flight Journal Article In: Aerospace Medicine and Human Performance, vol. 90, no. 2, pp. 92-100, 2019. @article{rice2019dry,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. |
2018 |
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. @article{vergeer2018orientation,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. |
2015 |
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. @conference{kohli2015towards,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 |
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