Electroencephalography (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain through electrodes placed on the scalp. EEG can be used for various research applications, including studying brain function and activity, identifying neurological disorders, and investigating the effects of drugs or other interventions on brain activity. EEG is particularly useful for studying brain activity in real-time and identifying the timing and location of brain activity associated with specific cognitive processes or behaviors. It can also be used in clinical settings to diagnose and monitor neurological disorders such as epilepsy, sleep disorders, and traumatic brain injuries. Additionally, EEG can be used to investigate the effects of various interventions, such as cognitive training or neurofeedback, on brain activity and function.
For surfers, catching the perfect wave can induce a state of pure ecstasy known as the “stoke”. But what’s happening in the brain during this ultimate ride? Wearable Sensing created a custom dry EEG system that measures brainwaves during surfing. They partnered with Red Bull to use this technology on professional surfers to uncover the neurophysiological aspects of surfing. The dry EEG system is worn on the head like a swimming cap, and it allows for the measurement of brain activity in real-time during surfing. By studying the brainwaves of surfers during their best rides, researchers hope to understand what goes on in the brain during moments of flow and peak performance, and ultimately unlock the secrets to achieving that elusive state of “stoke”.
In this study, wearable sensors and machine learning-based algorithms were used to predict hypoxia in-flight. The group used Wearable Sensing’s dry-EEG technology to collect sensor data from 85 participants during a two-phase study. Participants wore aviation flight masks, which regulated their oxygen intake while performing cognitive tests and simulated flying tasks. EEG data was collected and analyzed using principal component analysis and machine learning algorithms, including Naïve Bayes, decision tree, random forest, and neural network algorithms, to classify the data as normal or hypoxic. The results showed high sensitivity and specificity, indicating potential for developing a real-time, in-flight hypoxia detection system.
This paper proposes a protocol for assessing stress using wearable sensing technology, including Electroencephalography (EEG), Electrocardiography (ECG), and the Perceived Stress Scale, in combination with a Virtual Reality phobia induction setting. Wearable Sensing’s dry EEG technology is used to measure brain activity and investigate functional brain connectivity associated with stress. The proposed protocol can be expanded with the incorporation of machine learning algorithms for automatic stress level classification.
Mizrahi, Dor; Laufer, Ilan; Zuckerman, Inon
Modulation of Beta Power as a Function of Attachment Style and Feedback Valence Conference
International Conference on Brain Informatics, Springer 2023.
@conference{mizrahi2023modulation,
title = {Modulation of Beta Power as a Function of Attachment Style and Feedback Valence},
author = {Dor Mizrahi and Ilan Laufer and Inon Zuckerman},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43075-6_2},
year = {2023},
date = {2023-09-13},
urldate = {2023-01-01},
booktitle = {International Conference on Brain Informatics},
pages = {14–20},
organization = {Springer},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Georgiadis, Kostas; Kalaganis, Fotis P; Oikonomou, Vangelis P; Nikolopoulos, Spiros; Laskaris, Nikos A; Kompatsiaris, Ioannis
Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry Conference
International Conference on Brain Informatics, Springer 2023.
@conference{georgiadis2023harneshing,
title = {Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry},
author = {Kostas Georgiadis and Fotis P Kalaganis and Vangelis P Oikonomou and Spiros Nikolopoulos and Nikos A Laskaris and Ioannis Kompatsiaris},
url = {https://link.springer.com/chapter/10.1007/978-3-031-43075-6_3},
year = {2023},
date = {2023-09-13},
urldate = {2023-01-01},
booktitle = {International Conference on Brain Informatics},
pages = {21–32},
organization = {Springer},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Chiossi, Francesco; Turgut, Yagiz; Welsch, Robin; Mayer, Sven
Adapting Visual Complexity Based on Electrodermal Activity Improves Working Memory Performance in Virtual Reality Journal Article
In: Proc. ACM Hum.-Comput. Interact, vol. 7, 2023.
@article{chiossi2023adapting,
title = {Adapting Visual Complexity Based on Electrodermal Activity Improves Working Memory Performance in Virtual Reality},
author = {Francesco Chiossi and Yagiz Turgut and Robin Welsch and Sven Mayer},
url = {https://www.researchgate.net/profile/Francesco-Chiossi/publication/371539688_Adapting_Visual_Complexity_Based_on_Electrodermal_Activity_Improves_Working_Memory_Performance_in_Virtual_Reality/links/648983619bc5e436682f0b97/Adapting-Visual-Complexity-Based-on-Electrodermal-Activity-Improves-Working-Memory-Performance-in-Virtual-Reality.pdf},
year = {2023},
date = {2023-09-02},
urldate = {2023-01-01},
journal = {Proc. ACM Hum.-Comput. Interact},
volume = {7},
abstract = {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},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Georgiadis, Kostas; Kalaganis, Fotis P; Riskos, Kyriakos; Matta, Eleftheria; Oikonomou, Vangelis P; Yfantidou, Ioanna; Chantziaras, Dimitris; Pantouvakis, Kyriakos; Nikolopoulos, Spiros; Laskaris, Nikos A; others,
NeuMa-the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour Journal Article
In: Scientific Data, vol. 10, no. 1, pp. 508, 2023.
@article{georgiadis2023neuma,
title = {NeuMa-the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour},
author = {Kostas Georgiadis and Fotis P Kalaganis and Kyriakos Riskos and Eleftheria Matta and Vangelis P Oikonomou and Ioanna Yfantidou and Dimitris Chantziaras and Kyriakos Pantouvakis and Spiros Nikolopoulos and Nikos A Laskaris and others},
doi = {https://doi.org/10.1038/s41597-023-02392-9},
year = {2023},
date = {2023-08-03},
urldate = {2023-01-01},
journal = {Scientific Data},
volume = {10},
number = {1},
pages = {508},
publisher = {Nature Publishing Group UK London},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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,
title = {Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task},
author = {Margaret M Swerdloff and Levi J Hargrove},
url = {https://arinex.com.au/EMBC/pdf/full-paper_1084.pdf},
year = {2023},
date = {2023-07-01},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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
Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents Journal Article
In: Journal of Autism and Developmental Disorders, pp. 1–11, 2023.
@article{riek2023feedback,
title = {Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents},
author = {Nathan T Riek and Busra T Susam and Caitlin M Hudac and Caitlin M Conner and Murat Akcakaya and Jane Yun and Susan W White and Carla A Mazefsky and Philip A Gable},
url = {https://link.springer.com/article/10.1007/s10803-023-06038-y},
year = {2023},
date = {2023-07-01},
urldate = {2023-01-01},
journal = {Journal of Autism and Developmental Disorders},
pages = {1–11},
publisher = {Springer},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kim, Suhye; Kim, Jung-Hwan; Hyung, Wooseok; Shin, Suhkyung; Choi, Myoung Jin; Kim, Dong Hwan; Im, Chang-Hwan
Characteristic Behaviors of Elementary Students in a Low Attention State During Online Learning Identified Using Electroencephalography Journal Article
In: IEEE Transactions on Learning Technologies, 2023.
@article{kim2023characteristic,
title = {Characteristic Behaviors of Elementary Students in a Low Attention State During Online Learning Identified Using Electroencephalography},
author = {Suhye Kim and Jung-Hwan Kim and Wooseok Hyung and Suhkyung Shin and Myoung Jin Choi and Dong Hwan Kim and Chang-Hwan Im},
doi = {10.1109/TLT.2023.3289498},
year = {2023},
date = {2023-06-29},
urldate = {2023-01-01},
journal = {IEEE Transactions on Learning Technologies},
publisher = {IEEE},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cho, Ji Young; Wang, Ze-Yu; Hong, Yi-Kyung
An EEG Study on the Interior Environmental Design Elements in Promoting Healing Journal Article
In: Journal of the Korean Housing Association, vol. 34, no. 3, pp. 067–079, 2023.
@article{cho2023eeg,
title = {An EEG Study on the Interior Environmental Design Elements in Promoting Healing},
author = {Ji Young Cho and Ze-Yu Wang and Yi-Kyung Hong},
url = {https://journal.khousing.or.kr/articles/xml/XeZw/},
doi = {https://doi.org/10.6107/JKHA.2023.34.3.067},
year = {2023},
date = {2023-06-25},
urldate = {2023-01-01},
journal = {Journal of the Korean Housing Association},
volume = {34},
number = {3},
pages = {067–079},
publisher = {The Korean Housing Association},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Browne, Connor
Evaluating the Effectiveness of Preprocessing Methods on Motor Classification Scores in EEG Data Masters Thesis
University of Washington, 2023.
@mastersthesis{browne2023evaluating,
title = {Evaluating the Effectiveness of Preprocessing Methods on Motor Classification Scores in EEG Data},
author = {Connor Browne},
url = {https://www.proquest.com/openview/f610ae2952cdc715d3512cd8a8121b89/1?pq-origsite=gscholar&cbl=18750&diss=y},
year = {2023},
date = {2023-06-24},
urldate = {2023-07-24},
school = {University of Washington},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Fan, Tengwen; Zhang, Liming; Liu, Jianyi; Niu, Yanbin; Hong, Tian; Zhang, Wenfang; Shu, Hua; Zhao, Jingjing
Phonemic mismatch negativity mediates the association between phoneme awareness and character reading ability in young Chinese children Journal Article
In: Neuropsychologia, pp. 108624, 2023.
@article{fan2023phonemic,
title = {Phonemic mismatch negativity mediates the association between phoneme awareness and character reading ability in young Chinese children},
author = {Tengwen Fan and Liming Zhang and Jianyi Liu and Yanbin Niu and Tian Hong and Wenfang Zhang and Hua Shu and Jingjing Zhao},
doi = {https://doi.org/10.1016/j.neuropsychologia.2023.108624},
year = {2023},
date = {2023-06-15},
urldate = {2023-01-01},
journal = {Neuropsychologia},
pages = {108624},
publisher = {Elsevier},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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