DSI-Flex

@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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@article{li2026stmae,

title = {STMAE-Few: A Spatial-Temporal Masked Autoencoder for Few-channel EEG-based Emotion Recognition},

author = {Ming Li and Junjun Pan and Dai Ju and Wanqing Wu and Aimin Hao and Yong-Jin Liu and Yang Gao},

doi = {https://doi.org/10.1109/TAFFC.2026.3719828},

year  = {2026},

date = {2026-08-04},

urldate = {2026-01-01},

journal = {IEEE Transactions on Affective Computing},

publisher = {IEEE},

abstract = {Conventional EEG-based supervised emotion recognition typically requires large manually annotated multi-channel EEG data. The susceptibility of EEG to noise can lead to corruption of certain channels, making it challenging for emotion recognition from few available channels (denoted as few-channel in this paper). Masked Autoencoder (MAE) can learn highly generalizable representations by reconstructing the original signals from the visible subset of channels. However, most studies do not consider temporal masking, and random spatial masking overlooks the brain's inherent structure, impeding reconstruction efficacy. In this paper, we propose a novel pre-training framework, STMAE-Few, based on a spatial-temporal masked autoencoder, which introduces spatial-temporal masking strategies at both the EEG channel and frame levels to extract robust feature representations and enhance the performance of few-channel emotion recognition in real-world scenarios. Inspired by neuroscience, our approach integrates region and region-selection spatial masking strategies and introduces true position encoding during pre-training to capture richer spatial information. Extensive experimental results on SEED, SEED-IV, and SEED-V datasets demonstrate that our pre-training framework can effectively capture the temporal dynamics and spatial correlations within and between different brain regions.

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@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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pubstate = {published},

tppubtype = {article}

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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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pubstate = {published},

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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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