Research

Discover the Why of the Brain

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.

Applications

What Causes the "Stoke" in Surfing?

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”.

Predicting hypoxic hypoxia using machine learning and wearable sensors

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.

An experimental protocol for exploration of stress in an immersive VR scenario with EEG

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.

Hardware

DSI Dry EEG

All of Wearable Sensing's Dry EEG systems can be utilized for Brain Computer Interfaces

Learn More

NeuSenW High-Density EEG

All of Wearable Sensing's Wet EEG systems can be utilized for Brain Computer Interfaces

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Software

QStates

Machine learning algorithms for cognitive state classification, such as mental workload, engagement, and fatigue

Learn More

3rd Party Compatible Software

List of compatible software, including Neurofeedback, BCI, EEG Analysis, SDK's, and more

Learn More

Publications

215 entries « ‹ 1 of 22 › »

2026

Kim, Heegyu; Jeon, Gyewon; Lee, Sangwon; Jun, Sung Chan; Jung, Jae-Yoon; Nam, Chang S

The dynamics of beliefs in social decision-making: insights from intra-and inter-brain neural networks Journal Article

In: Scientific Reports, 2026.

Abstract | Links | BibTeX

@article{kim2026dynamics,

title = {The dynamics of beliefs in social decision-making: insights from intra-and inter-brain neural networks},

author = {Heegyu Kim and Gyewon Jeon and Sangwon Lee and Sung Chan Jun and Jae-Yoon Jung and Chang S Nam},

doi = {https://doi.org/10.1038/s41598-026-58455-5},

year  = {2026},

date = {2026-08-21},

urldate = {2026-01-01},

journal = {Scientific Reports},

publisher = {Nature Publishing Group UK London},

abstract = {Social beliefs are internalized expectations about others’ behaviors and intentions within social systems, shaping how individuals interpret, predict, and respond to interpersonal events. This study aims to examine how the dynamics of social beliefs influence neural activity and inter-brain synchrony within group interactions. To quantitatively assess relational changes, we implemented an iterated 3-player Prisoner’s Dilemma game paradigm, allowing for the observation of group-level behavioral patterns across repeated cooperation and defection scenarios. Participants were repeatedly exposed to cooperative and non-cooperative interactions, during which we observed neurophysiological responses associated with beliefs formation and change. Event-Related Potential analysis was used to assess individual cognitive responses to beliefs shifts, with P300 and N100 components reflecting sensitivity to social feedback. Additionally, intra-brain network analysis revealed changes centered around the parietal region, indicating altered social relationship representations at the individual level. At the group level, inter-brain synchrony significantly declined following beliefs changes, reflecting a disruption in shared intentionality and coordination. These findings suggest that violations of social expectations may induce cognitive instability and reduce neural synchrony in group decision-making contexts, offering new insights into the neural mechanisms underlying dynamic social cognition.



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Marquardt, Ingo; Alchanat, Anthilia; Jain, Priyanka

Decoding silent reading from non-invasive EEG Technical Report

2026.

Abstract | Links | BibTeX

@techreport{marquardt2026decoding,

title = {Decoding silent reading from non-invasive EEG},

author = {Ingo Marquardt and Anthilia Alchanat and Priyanka Jain},

doi = {https://doi.org/10.48550/arXiv.2608.20186},

year  = {2026},

date = {2026-08-20},

urldate = {2026-01-01},

journal = {arXiv preprint},

abstract = {Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable. We therefore treat silent reading as a scalable proxy task and ask how much lexical and semantic information a contrastive decoder can extract from it. We report an open-vocabulary analysis of approximately 240,000 word presentations recorded from a single densely-sampled participant across 393 runs (ca. 49 h) of 19-channel dry-electrode EEG. Words from continuous narrative text were presented in rapid serial visual presentation, with typography randomised on every trial to partially decorrelate word identity from low-level visual form. A convolutional EEG encoder, optionally followed by a causal transformer, was trained with a CLIP-style contrastive objective to align short EEG windows with hidden-state embeddings of the presented word taken from a large language model. Decoding, evaluated as word-grouped top-10 retrieval against permutation baselines, was reliably above chance, extended to mid-frequency and rare words, and scaled log-linearly with training-data volume with no sign of saturation. Removing occipital and posterior-temporal electrodes reduced the word-level gain by roughly one third but left context tracking unchanged. Control analyses separate word-level decoding from narrative context tracking and from a non-neural positional prior introduced by the transformer's positional embedding. These results establish that open-vocabulary word-level information is recoverable from EEG during silent reading, and that decoding is data-limited rather than saturated.

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Leuthardt, Eric C; Wilk, Seth J; Souders, Lauren; Carr, Kelly B; Coker, Robert; Wilding, Gregory E; Zuleger, Dorothee; Acland, Benjamin T; Carter, Alexandre R

At-Home BCI Rehabilitation Therapy for Chronic Upper Extremity Deficit After Stroke (BCI-REHAB): A Randomized Trial Journal Article

In: Stroke: Vascular and Interventional Neurology, pp. 10–1161, 2026.

Abstract | Links | BibTeX

@article{leuthardt2026home,

title = {At-Home BCI Rehabilitation Therapy for Chronic Upper Extremity Deficit After Stroke (BCI-REHAB): A Randomized Trial},

author = {Eric C Leuthardt and Seth J Wilk and Lauren Souders and Kelly B Carr and Robert Coker and Gregory E Wilding and Dorothee Zuleger and Benjamin T Acland and Alexandre R Carter},

doi = {https://doi.org/10.1161/SVIN.126.002345},

year  = {2026},

date = {2026-08-13},

urldate = {2026-01-01},

journal = {Stroke: Vascular and Interventional Neurology},

pages = {10–1161},

publisher = {Lippincott Williams & Wilkins Hagerstown, MD},

abstract = {BACKGROUND:

At-home stroke rehabilitation methods have strong potential to supplement limited clinical resources, but realizing that potential for survivors in the chronic phase of recovery requires developing and validating more effective options than the home exercise programs typically used today. BCI-REHAB (At-Home BCI Rehabilitation Therapy for Chronic Upper Extremity Deficit After Stroke) compared an at-home brain-computer interface (BCI) therapy system (IpsiHand System) versus an at-home exercise program for improving upper extremity function in patients with chronic hemiparetic stroke.

METHODS:

Participants aged 18 to 85 years with a history of stroke ≥6 months before enrollment and right or left upper extremity paresis or plegia were screened and randomly assigned to either an at-home exercise program or to a BCI electroencephalogram system coupled to a range-of-motion assist handpiece. Participants completed 12 weeks of at-home therapy (5 sessions per week). The primary outcome was the change in the Upper Extremity Fugl-Meyer Assessment from baseline to 12 weeks.

RESULTS:

Overall, 109 participants were assessed for eligibility; 85 met the inclusion criteria and were randomized (43 intervention, 42 control). Of the 42 control participants, n=17 declined to participate further due to dissatisfaction with study arm assignment. A total of 62 participants were analyzed for the primary outcome (37 intervention, 25 control). The primary outcome (mean change) was significantly higher in the BCI group (6.0 [95% CI, 3.9–8.1]; P<0.0001) versus the control group (1.5 [95% CI, −0.1 to 3.1]; P=0.07), with an estimated treatment difference of 4.5 ([95% CI, 1.9–7.1]; P=0.0007). Participants who received the BCI intervention showed an estimated response rate of 55.5% (95% CI, 33.7%–77.2%), compared with 9.6% (95% CI, −2.9% to 22.1%) in the control group. This corresponds to an absolute increase in response of 45.8% ([95% CI, 20.9%–70.8%]; P=0.0003), yielding a number needed to treat of 2.2.

CONCLUSIONS:

At-home BCI therapy provides clinically meaningful improvement in upper extremity function for chronic stroke survivors compared with standard at-home exercise programs.},

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pubstate = {published},

tppubtype = {article}

}

BACKGROUND:
At-home stroke rehabilitation methods have strong potential to supplement limited clinical resources, but realizing that potential for survivors in the chronic phase of recovery requires developing and validating more effective options than the home exercise programs typically used today. BCI-REHAB (At-Home BCI Rehabilitation Therapy for Chronic Upper Extremity Deficit After Stroke) compared an at-home brain-computer interface (BCI) therapy system (IpsiHand System) versus an at-home exercise program for improving upper extremity function in patients with chronic hemiparetic stroke.
METHODS:
Participants aged 18 to 85 years with a history of stroke ≥6 months before enrollment and right or left upper extremity paresis or plegia were screened and randomly assigned to either an at-home exercise program or to a BCI electroencephalogram system coupled to a range-of-motion assist handpiece. Participants completed 12 weeks of at-home therapy (5 sessions per week). The primary outcome was the change in the Upper Extremity Fugl-Meyer Assessment from baseline to 12 weeks.
RESULTS:
Overall, 109 participants were assessed for eligibility; 85 met the inclusion criteria and were randomized (43 intervention, 42 control). Of the 42 control participants, n=17 declined to participate further due to dissatisfaction with study arm assignment. A total of 62 participants were analyzed for the primary outcome (37 intervention, 25 control). The primary outcome (mean change) was significantly higher in the BCI group (6.0 [95% CI, 3.9–8.1]; P<0.0001) versus the control group (1.5 [95% CI, −0.1 to 3.1]; P=0.07), with an estimated treatment difference of 4.5 ([95% CI, 1.9–7.1]; P=0.0007). Participants who received the BCI intervention showed an estimated response rate of 55.5% (95% CI, 33.7%–77.2%), compared with 9.6% (95% CI, −2.9% to 22.1%) in the control group. This corresponds to an absolute increase in response of 45.8% ([95% CI, 20.9%–70.8%]; P=0.0003), yielding a number needed to treat of 2.2.
CONCLUSIONS:
At-home BCI therapy provides clinically meaningful improvement in upper extremity function for chronic stroke survivors compared with standard at-home exercise programs.

Li, Ming; Pan, Junjun; Ju, Dai; Wu, Wanqing; Hao, Aimin; Liu, Yong-Jin; Gao, Yang

STMAE-Few: A Spatial-Temporal Masked Autoencoder for Few-channel EEG-based Emotion Recognition Journal Article

In: IEEE Transactions on Affective Computing, 2026.

Abstract | Links | BibTeX

Celik, Basak; Huang, Catherine; Madison, Cindee; Mathan, Santosh; Gipson, Bryant; Smart, Andrew J; Erdogmus, Deniz

RSVP-induced ERP Class Posteriors Reflect Graded Neural Perception of Humanoid Images Conference

9th Annual Conference on Cognitive Computational Neuroscience, 2026.

Abstract | Links | BibTeX

@conference{celikrsvp,

title = {RSVP-induced ERP Class Posteriors Reflect Graded Neural Perception of Humanoid Images},

author = {Basak Celik and Catherine Huang and Cindee Madison and Santosh Mathan and Bryant Gipson and Andrew J Smart and Deniz Erdogmus},

url = {https://openreview.net/forum?id=qvzdqR9sYX},

year  = {2026},

date = {2026-07-31},

booktitle = {9th Annual Conference on Cognitive Computational Neuroscience},

abstract = {In the rapid serial visual presentation (RSVP) paradigm, EEG event-related potentials (ERPs) are captured when a target stimulus appears in the stimulus sequence. In the context of detecting target objects (e.g. humans) in images, it is uncertain whether an ERP will manifest in EEG readings when a sequence includes a small ratio of pseudotargets (e.g. non-human but human-shaped humanoids) interspersed with actual human targets. We studied this question through a binary EEG classification model that yields target perception scores for human presence in images. This model produces a value in [0,1] depending on image content, from EEG time-locked to image onset in RSVP. The model was calibrated using a set of EEG responses to a combination of distractor (nontarget) and target images shown to multiple subjects within the RSVP paradigm. Target class posterior given EEG temporally aligned to image onset was used as a target perception score. Analyses on test imagery demonstrated that, as anticipated, distractor images result in EEG signals that tend to receive lower target perception scores than that of target images. More interestingly, pseudotarget (humanoid) test images that were intentionally designed to induce confusion received slightly lower target perception scores than target images. While the analyses and observations are based on data from limited number of subjects and trials, the results are promising as they indicate degraded target recognition response to humanoid images in contrast to genuine targets.



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Matavalam, Chandu Priya; Gangadharan, Sagila; Vinod, AP

Consumer Trait Prediction from EEG Using a Novel SpectralTraitNet for Neuromarketing Application Conference

2026 IEEE International Conference on Human-Machine Systems (ICHMS), IEEE 2026.

Abstract | Links | BibTeX

@conference{matavalam2026consumer,

title = {Consumer Trait Prediction from EEG Using a Novel SpectralTraitNet for Neuromarketing Application},

author = {Chandu Priya Matavalam and Sagila Gangadharan and AP Vinod},

doi = {https://doi.org/10.1109/ICHMS69701.2026.11602183},

year  = {2026},

date = {2026-07-15},

urldate = {2026-01-01},

booktitle = {2026 IEEE International Conference on Human-Machine Systems (ICHMS)},

pages = {416–421},

organization = {IEEE},

abstract = {Decoding stable consumer traits from Electroencephalography (EEG) remains a largely unexplored frontier in neuromarketing, which has traditionally focused on short-term preference classification (e.g., “Like” vs. “Dislike”). Moving beyond transient reactions to identifying long-term profiles, such as the “Bargain Hunter”- a consumer predominantly motivated by finding the best deals and discounts to make purchase decisions, enables marketers to target promotions more effectively to drive conversions. However, consumer trait prediction is challenging due to the subtle nature of trait-based neural signatures, intra-subject label homogeneity, and significant inter-subject variability. Unlike conscious preference tasks, consumer traits function as subconscious priors, resulting in a high risk of the model overfitting to individual identities rather than generalized traits. In this study, we propose a subject-independent deep learning framework to classify “Bargain Hunter” vs. “Non-Bargain Hunter” traits using EEG signals recorded during product browsing. A hybrid lightweight Convolutional Neural Network derived by combining EEGNet and MobileNet, called SpectralTraitNet, which uses depthwise separable convolutions and attention mechanism, is proposed to extract discriminative spatial-spectral features. To enhance cross-subject generalization, Euclidean Alignment is employed to align subject-specific covariance matrices into a common manifold, effectively addressing domain shifts. The framework is evaluated on the publicly available NeuMa dataset (N=42), and a subject-level classification accuracy of 6 4. 2 9±1 8 % is achieved using rigorous 6 -fold stratified cross-validation. These results demonstrate that manifold alignment and data augmentation are critical for extracting generalized features across unseen subjects, offering a pathway towards more personalized, neurologically informed pricing and marketing strategies.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

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Kim, H; Jun, SC; Nam, CS

A reproducible EEG hyperscanning dataset for triadic social decision-making during an iterated 3-player Prisoner’s Dilemma Journal Article

In: Data in Brief, pp. 113064, 2026.

Abstract | Links | BibTeX

Debnath, Shubham; Fylaktou, Fylaktis; Gurfein, Blake T; Zanos, Theodoros P

Autonomic and neural responses to varying transcutaneous cervical electrical stimulation parameters Journal Article

In: Bioelectronic Medicine, vol. 12, no. 1, pp. 16, 2026.

Abstract | Links | BibTeX

@article{debnath2026autonomic,

title = {Autonomic and neural responses to varying transcutaneous cervical electrical stimulation parameters},

author = {Shubham Debnath and Fylaktis Fylaktou and Blake T Gurfein and Theodoros P Zanos},

doi = {https://doi.org/10.1186/s42234-026-00210-2},

year  = {2026},

date = {2026-07-06},

urldate = {2026-07-06},

journal = {Bioelectronic Medicine},

volume = {12},

number = {1},

pages = {16},

publisher = {Springer},

abstract = {Background

Transcutaneous cervical electrical stimulation (TCES) offers a noninvasive approach to modulate the autonomic nervous system (ANS), but optimal stimulation parameters remain undefined. This pilot study aimed to identify optimal TCES parameters by evaluating autonomic and neural responses across varying frequencies, current intensities, electrode montages, and durations, using heart rate variability (HRV) and electroencephalography (EEG) alpha-band power as biomarkers of parasympathetic activity.



Methods

Twenty healthy adults completed four testing sessions, each examining one stimulation parameter. Autonomic data were collected including electrocardiography, non-invasive blood pressure, pupillometry, photoplethysmography, and dry-electrode EEG. Four frequencies (10, 25, 40, 150 Hz), three current intensities (sub-sensation threshold, sensation threshold, supra-sensation threshold), three electrode montages (bilateral, left-only, right-only), and two durations (4, 20 min) were tested. Root mean square of successive differences (RMSSD) and global EEG alpha-band power were primary outcomes. Parameters were sequentially optimized across visits based on individual RMSSD responses.



Results

No single frequency produced a significantly higher RMSSD or alpha-band power response. However, each participant exhibited a personalized preferred frequency yielding a mean 41% RMSSD increase in visit 1. This individualized frequency was selected for further visits varying current intensity and electrode montage. Supra-sensation threshold intensity was most effective, with 60% of participants responding strongest at this level. Left-sided stimulation resulted in a decrease in both RMSSD and alpha-band power, while right-sided and bilateral montages resulted in similar increases for these biomarkers. Due to decreasing cardiac vagal response in successive sessions, the preferred frequency was reevaluated before testing duration. The mean RMSSD response increased 54% upon recalibration in visit 4, though the preferred frequency shifted in 75% of participants. Autonomic vitals did not significantly modulate more with longer stimulation duration; pulse rate variability during 20-min stimulation revealed oscillatory autonomic dynamics with peak parasympathetic responses emerging around 4 min.



Conclusions

TCES can modulate cardiac vagal and cortical responses as measured by RMSSD and EEG alpha-band power, respectively, and a personalized, biomarker-guided approach to TCES parameter optimization is essential for future clinical applications targeting autonomic dysfunction.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Background
Transcutaneous cervical electrical stimulation (TCES) offers a noninvasive approach to modulate the autonomic nervous system (ANS), but optimal stimulation parameters remain undefined. This pilot study aimed to identify optimal TCES parameters by evaluating autonomic and neural responses across varying frequencies, current intensities, electrode montages, and durations, using heart rate variability (HRV) and electroencephalography (EEG) alpha-band power as biomarkers of parasympathetic activity.

Methods
Twenty healthy adults completed four testing sessions, each examining one stimulation parameter. Autonomic data were collected including electrocardiography, non-invasive blood pressure, pupillometry, photoplethysmography, and dry-electrode EEG. Four frequencies (10, 25, 40, 150 Hz), three current intensities (sub-sensation threshold, sensation threshold, supra-sensation threshold), three electrode montages (bilateral, left-only, right-only), and two durations (4, 20 min) were tested. Root mean square of successive differences (RMSSD) and global EEG alpha-band power were primary outcomes. Parameters were sequentially optimized across visits based on individual RMSSD responses.

Results
No single frequency produced a significantly higher RMSSD or alpha-band power response. However, each participant exhibited a personalized preferred frequency yielding a mean 41% RMSSD increase in visit 1. This individualized frequency was selected for further visits varying current intensity and electrode montage. Supra-sensation threshold intensity was most effective, with 60% of participants responding strongest at this level. Left-sided stimulation resulted in a decrease in both RMSSD and alpha-band power, while right-sided and bilateral montages resulted in similar increases for these biomarkers. Due to decreasing cardiac vagal response in successive sessions, the preferred frequency was reevaluated before testing duration. The mean RMSSD response increased 54% upon recalibration in visit 4, though the preferred frequency shifted in 75% of participants. Autonomic vitals did not significantly modulate more with longer stimulation duration; pulse rate variability during 20-min stimulation revealed oscillatory autonomic dynamics with peak parasympathetic responses emerging around 4 min.

Conclusions
TCES can modulate cardiac vagal and cortical responses as measured by RMSSD and EEG alpha-band power, respectively, and a personalized, biomarker-guided approach to TCES parameter optimization is essential for future clinical applications targeting autonomic dysfunction.

Xu, Chi; Li, Xiang; Shehata, Allam; Aljazaerly, Mohamad Ammar Alsherfawi; Yagi, Yasushi

OU-MB: The OU Multimodal Biometric Database and Its Performance Evaluation Journal Article

In: IEEE Transactions on Biometrics, Behavior, and Identity Science, 2026.

Abstract | Links | BibTeX