Research

@article{kim2026reproducible,

title = {A reproducible EEG hyperscanning dataset for triadic social decision-making during an iterated 3-player Prisoner’s Dilemma},

author = {H Kim and SC Jun and CS Nam},

doi = {https://doi.org/10.1016/j.dib.2026.113064},

year  = {2026},

date = {2026-07-10},

urldate = {2026-01-01},

journal = {Data in Brief},

pages = {113064},

publisher = {Elsevier},

abstract = {This data article describes an open EEG hyperscanning dataset acquired during an iterated 3-player Prisoner’s Dilemma (PD) task, designed to support reproducible research on triadic social decision-making. EEG was recorded simultaneously from three participants per group (11 groups; 33 subjects) using synchronized acquisition, with decision-locked and feedback-locked epochs provided for 40 task trials per subject and three 60-s resting-state runs per group. The released MATLAB files contain stacked 57-channel EEG arrays (19 channels × 3 subjects) sampled at 300 Hz, with explicit epoch definitions for decision (−1000 to 4000 ms) and feedback (−1000 to 2000 ms) periods, as well as trial-wise behavioral choice labels (1=cooperate, 2=defect) enabling reconstruction of dyad- and triad-level outcomes. Questionnaire metadata (personal information and pre/mid/post state measures) Yare also provided as supplementary spreadsheets. To facilitate reuse and benchmarking, we release Python code that reproduces the preprocessing pipeline (average reference, FIR filtering, ICA + ICLabel for task EEG), ERP summaries, and inter-brain synchrony measures (PLV and coherence) with window-matched resting baselines and cluster-permutation statistics. The dataset is intended for method development and benchmarking in ERP analysis, inter-brain synchrony estimation, and modeling of dynamic group interaction states in triadic games.},

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}

@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.},

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@article{xu2026mb,

title = {OU-MB: The OU Multimodal Biometric Database and Its Performance Evaluation},

author = {Chi Xu and Xiang Li and Allam Shehata and Mohamad Ammar Alsherfawi Aljazaerly and Yasushi Yagi},

doi = {https://doi.org/10.1109/TBIOM.2026.3710514},

year  = {2026},

date = {2026-07-06},

urldate = {2026-01-01},

journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},

publisher = {IEEE},

abstract = {In this paper, we describe a new multimodal biometric database named “the University of Osaka Multimodal Biometric Database (OU-MB)”. This database consists of 1,099 subjects and eleven biometric modalities, which, to the best of our knowledge, is the largest number of modalities among existing multimodal databases. Specifically, for each subject, we collected his/her irises, palm veins, 2D face images, signature images, gait videos, and voice data, which are typically included in existing multimodal databases. Additionally, some modalities not commonly considered in previous datasets are also included, that is, full-body images, online signature time series data, brain signals, inertial data (e.g., acceleration), and health data (e.g., heartbeat). We provide comprehensive baseline evaluations across all eleven biometric modalities included in OU-MB and further investigate multimodal recognition through representative score-level and feature-level fusion experiments. We believe this database can facilitate future research on person authentication using unimodal, multimodal, and even cross-modal approaches, as well as research on brain signal and health status analysis.

},

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@article{jeong2026psychological,

title = {Psychological Empowerment on the Streets: Designing and Validating Multisensory Experiences in Simulated Autonomous Driving},

author = {Eunju Jeong and You Jeong Hong and Jiyeon Shin and Jong Su Kim and Moon A Yoo and Sung-Phil Kim},

doi = {https://doi.org/10.1111/nyas.70305},

year  = {2026},

date = {2026-06-16},

urldate = {2026-01-01},

journal = {Annals of the New York Academy of Sciences},

volume = {1560},

number = {1},

pages = {e70305},

publisher = {Wiley Online Library},

abstract = {Driving is evolving from a transportation task into a rich, multisensory experience in automated vehicles. This study developed and evaluated three multisensory solutions combining music with synchronized vibrotactile stimulation for autonomous driving contexts: safe (city driving), engagement (highway cruising), and entertainment (highway entry). Eighteen healthy adult drivers experienced three context–solution pairs presented in music only (M) and music with vibration (MV) modalities with simulated autonomous driving scenarios. Participants’ responses were measured using self-assessment manikin (SAM) ratings and electroencephalography (EEG). A significant main effect of modality showed that MV led to greater pleasure than M (EEG: p < 0.05; SAM: p < 0.05), with arousal showing a similar pattern (EEG: p < 0.05; SAM: p = 0.099). Behavioral data showed different emotional profiles across the three context−solution pairs (p < 0.001 for pleasure, arousal, and dominance), whereas the EEG contrast, which subtracted the video-only condition, showed no significant pair effect. These findings demonstrate that vibrotactile enhancement provides consistent emotional benefits across diverse driving contexts. Because each musical solution was paired with a unique driving scenario, these differences cannot be attributed solely to the music intervention. Future optimization of music and vibrotactile parameters may further enhance the autonomous driving experience.},

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@article{susam2026neural,

title = {Neural effects of meditation following a randomized controlled trial of the Emotion Awareness and Skills Enhancement (EASE)},

author = {Busra T Susam and Nathan T Riek and Richard T Gall and Caitlin M Conner and Susan W White and Carla A Mazefsky and Murat Akcakaya},

doi = {https://doi.org/10.1109/TNSRE.2026.3698744},

year  = {2026},

date = {2026-06-01},

urldate = {2026-01-01},

journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},

publisher = {IEEE},

abstract = {Mindfulness has promise for enhancing emotion regulation in autistic individuals. Electroencephalography (EEG) emerges as an ideal, objective measure of neural responses before and after mindfulness practice. In a study with 39 autistic individuals, EEG analysis assessed the impact of mindfulness in the participants in a randomized controlled clinical trial of the Emotion Awareness and Skills Enhancement (EASE) program compared to a control group. Participants completed an affective Posner Task while wearing an EEG headset during two visits. In between visits, each participant either received EASE or active control therapy. Using random forest classifiers over the Post-Visit EEG features baseline corrected by Pre-Visit, the study achieved 88.48% to 91.86% accuracy in distinguishing EASE and Control groups under distress, non-distress, and neutral conditions. Linear mixed-effects models applied across the full EEG feature set revealed significant Visit × Feedback × Group interactions in central theta and midline and occipital beta band power, with post-hoc analyses indicating these effects were primarily driven by differential neural responses to rewarding versus unfavorable feedback. The EASE group demonstrated distinct pre-to-post changes in these features relative to the Control group, suggesting intervention-related modulation of neural systems supporting adaptive responses to emotionally meaningful feedback. These findings underscore mindfulness' positive influence on emotion regulation potentially showing its effects as neural oscillations matching the results from existing literature. Subgroup analysis based on Clinical Global Impressions threshold scores identified responders and non-responders with LME analyses confirming greater intervention-related EEG changes in EASE responders compared to Control responders.Our findings provide valuable insights into potential benefits of mindfulness-based interventions for autistic people, highlighting the neurophysiological effects of such programs.

},

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@article{ishmakhametov2026multimodal,

title = {A multimodal dataset for emotional transition analysis in virtual reality},

author = {Namazbai Ishmakhametov and Mohammad YM Naser and Selam B Kelil and Charles McClary and Jason S Metcalfe and Sylvia Bhattacharya},

doi = {https://doi.org/10.1038/s41597-026-07456-0},

year  = {2026},

date = {2026-05-20},

urldate = {2026-01-01},

journal = {Scientific Data},

publisher = {Nature Publishing Group UK London},

abstract = {Emotion recognition from physiological signals typically treats emotions as discrete, static states rather than dynamic processes, limiting real-world affective computing applications. This dataset contains multimodal physiological recordings from 28 participants experiencing systematically designed emotional transitions in virtual reality. Participants viewed emotion-eliciting video stimuli across three emotional quadrants with transition periods between stimuli. Four physiological modalities were recorded: EEG (7 channels, 300 Hz), ECG (4 leads, 512 Hz), EMG (2 channels, 512 Hz), and GSR (3 channels, 10 Hz). The protocol employed a balanced incomplete block design across six emotional sequences. Statistical validation shows quadrant differentiation with 70% physiologically validated and 85% self-reported emotion induction success rates on average. Individual journey analysis indicates that participants traversed between 8.84% and 58.39% of the theoretical maximum cumulative distance on the valence–arousal plane, reflecting substantial individual differences in emotional responsivity. The dataset comprises 1.84 GB of original XDF recordings, 238 video-aligned physiological segments, and self-assessment ratings. This resource enables research in dynamic emotion recognition, and individual differences in responsivity during controlled emotional transitions.



},

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@article{fincham2026combining,

title = {Combining EEG signals from the 2 members of a team to improve event identification},

author = {Jon M Fincham and Shawn Betts and John R Anderson},

doi = {https://doi.org/10.1016/j.ynirp.2026.100356},

year  = {2026},

date = {2026-05-20},

urldate = {2026-05-20},

journal = {NeuroImage: Reports},

volume = {6},

number = {2},

pages = {100356},

publisher = {Elsevier},

abstract = {We examined the potential of combining EEG signals from multiple individuals to identify critical events in a team task. In this study two subjects played a video game in which they had complementary roles, one player serving as a Bait to distract 5 enemy fortress and the other serving as a Shooter to destroy the fortress. Twenty-one pairs of subjects were analyzed. Critical events, destruction of the fortress and deaths of each player, evoked distinguishable P300-like responses from both players. Fortress kills could be best identified by combining the two EEG signals, while deaths could be best identified by focusing on the response of the player who died. Hidden semi-Markov models (HSMMs) achieved good identification of the events by combining information about the temporal distribution of these critical events with the conditional probability of the EEG activity. These findings indicate that we can track and improve by adaptively merging or selecting the signals from different team members.

},

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@article{gravunder2026novel,

title = {Novel Time-Series Forecasting Method to Enhance Accuracy of Real-Time EEG Detection for BCI-Based Neurofeedback Motor Training in Individuals with Cerebral Palsy and Other Neurological Disorders},

author = {Andrew Gravunder and Amanda Studnicki and Julia Kline and Ahad Behboodi and Thomas C Bulea and Diane L Damiano},

doi = {https://doi.org/10.3390/bioengineering13050561},

year  = {2026},

date = {2026-05-15},

urldate = {2026-01-01},

journal = {Bioengineering},

volume = {13},

number = {5},

pages = {561},

publisher = {MDPI},

abstract = {Real-time detection of motor intent using electroencephalography (EEG) with high accuracy remains a technical challenge for neurorehabilitation. Brain–computer interface-based neurofeedback training (BCI-NFT) paradigms need to detect pre-movement EEG to activate robotics or electrical stimulation nearly simultaneously with movement to promote neuroplasticity. We present a novel detection method commonly used in time-series forecasting (e.g., stock market trends), identifying crosses in fast (short) and slow (long) moving average windows to identify negative deflections in slow movement-related cortical potentials (MRCPs) or event-related desynchronization (ERD) within −400–+100 ms of movement onset. We recorded EEG data from the Cz electrode during our cued ankle dorsiflexion BCI-NFT paradigm in four adult participants, two neurotypical and two with cerebral palsy. Simulated real-time offline analyses demonstrated an 85.9% mean true positive rate and 14.1% false positive rate of detecting motor intent at a mean −182 ms from movement onset. We further evaluated whether the detection indicated a MRCP and/or ERD, with MRCP detected in 70–80% of trials in three participants, but high ERD detection (87%) instead in the other. Preliminary results indicate that this approach offers a straightforward, accurate, and well-timed method for real-time EEG detection during neurofeedback training and as a control signal for brain–computer interfaces.

},

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