DSI-Flex

@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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {2026-01-01},

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.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lyons2026human,

title = {Human brain changes after first psilocybin use},

author = {T Lyons and M Spriggs and L Kerkelä and FE Rosas and L Roseman and PAM Mediano and C Timmermann and L Oestreich and BA Pagni and RJ Zeifman and R. L. Carhart-Harris and others},

doi = {https://doi.org/10.1038/s41467-026-71962-3},

year  = {2026},

date = {2026-05-06},

urldate = {2026-01-01},

journal = {Nature Communications},

volume = {17},

number = {1},

pages = {3977},

publisher = {Nature Publishing Group UK London},

abstract = {Psychedelics have robust effects on acute brain function and long-term behavior but whether they also cause enduring functional and anatomical brain changes is largely unknown. In an exploratory, placebo-controlled, within-subjects, electroencephalography (EEG), and magnetic resonance imaging (MRI) study in 28 healthy, entirely psychedelic-naive participants, anatomical and functional brain changes are detected from one-hour to one-month after a single high-dose (25 mg) of psilocybin. Increases in cognitive flexibility, psychological insight, and well-being are seen at one-month. Diffusion tensor imaging (DTI) done before and one-month after 25 mg psilocybin reveals decreased axial diffusivity bilaterally in prefrontal-subcortical tracts that correlate with decreases in brain network modularity (fMRI) over the same month. Enduring functional brain changes are largely absent, but network modularity change (numerical decrease) negatively correlates with well-being change (significant increase), in line with previous findings in depression. Increased cortical signal entropy (EEG) at 1- and 2-hours post-dosing predicts improved psychological well-being at one-month. Next-day psychological insight mediates the entropy to well-being relationship. All effects are exclusive to 25 mg psilocybin; no effects occur with a 1 mg psilocybin placebo.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yoo2026counselor,

title = {Counselor gender and scenario severity in virtual reality counseling: a neurophysiological and behavioral analysis for female university students in South Korea},

author = {Seungchul Yoo and Jiyoung Kang and Jungho Suh},

doi = {https://doi.org/10.1016/j.jbct.2026.100597},

year  = {2026},

date = {2026-05-01},

urldate = {2026-01-01},

journal = {Journal of Behavioral and Cognitive Therapy},

volume = {36},

number = {4},

pages = {100597},

publisher = {Elsevier},

abstract = {Intro

Virtual reality (VR)–based counseling is an emerging modality for digital mental health, offering immersive environments that can be experimentally controlled to test how social cues shape responses to therapeutic content. However, limited research has examined how counselor avatar gender and scenario severity jointly influence observers’ affective outcomes and neurophysiological dynamics during vicarious exposure to VR counseling.



Methods

Fifty-one female college students (ages 18‒25) from South Korea were randomly assigned to a 2 × 2 factorial design: counselor avatar gender (male vs. female) × scenario severity (mild-stressor vs. severe-crisis). Participants observed a scripted VR counseling scenario (∼12 min analyzable exposure; ∼20 min total including setup/instructions). Pre- and post-session assessments included State-Trait Anxiety Inventory (STAI-S; total scores 20–80) and Warwick-Edinburgh Mental Well-Being Scale (WEMWBS; total scores 14–70). Relative beta power (13–30 Hz) was measured via EEG (Lovibond and Lovibond, 1995).



Results

A 2 × 2 ANOVA on post-session STAI-S total scores revealed a significant counselor gender × scenario severity interaction, F(1,47) = 8.50, p = 0.005, η2 = 0.153. Participants observing severe-crisis scenarios with female counselors reported highest anxiety (M = 72.0, SD = 9.8) versus male counselors (M = 48.8, SD = 18.8), t(22) = 3.58, p = 0.002, d = 1.50. For WEMWBS, severe-scenario participants showed greater well-being with female counselors (M = 52.1, SD = 10.6) than male (M = 40.0, SD = 12.0), t(22) = 2.68, p = 0.014, d = 1.08. EEG revealed no main effect of group (p = 0.754), but a significant Group × Channel interaction (p = 0.014) in frontal-temporal regions.



Conclusion.

Counselor-avatar gender cues and scenario severity shaped observers’ immediate affective and neurophysiological responses to simulated VR counseling in complex ways. Elevated anxiety during severe-crisis content—particularly when observing a female-coded counselor avatar—may reflect intensified engagement with distressing material rather than simple discomfort. Neurophysiological findings indicate phase-dependent changes in arousal-related processing, with condition effects appearing regionally localized rather than global. These findings may inform future research on avatar design in digital mental health environments and motivate direct comparisons between observational and interactive VR counseling paradigms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jiangbrain,

title = {Brain-computer interface research on the visualization and analysis of sports rehabilitation training systems supported by artificial intelligence},

author = {Yun Jiang and Peng Huang},

url = {https://ingegneriasismica.com/articles/2026/IS-B013.pdf},

year  = {2026},

date = {2026-05-01},

journal = {International Journal of Earthquake Engineering},

volume = {Anno XLIII },

number = {Num. 2 },

abstract = {In the study, the EEG signals are firstly acquired and processed, and then a multidomain fusion feature extraction algorithm is proposed, which fuses two algorithms, Improved Localized Feature Scale Decomposition (ILCD) and Adaptive Common Spatial Patterns (ACSP), to extract the features of both time-frequency and spatial domains. In order to improve the classification ability of MI signals, a convolutional neural network model based on spatial self-attention and multi-timescale feature extraction is designed to realize the classification of MI signals under motion by introducing multi-scale feature extraction and attention mechanism. Finally, a rehabilitation training system based on the algorithm of this paper was designed using mixed programming in Matlab and C. Subjects were selected for validation. The experimental results show that in the actual experiments with several subjects, the classification accuracy of this paper's algorithm is up to 82%, and the average classification accuracy is 62.19%, and the rehabilitation training system built by the research can accurately extract the user's EEG signals in real time and accurately control the movement of the rehabilitation robot according to the user's imagination.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PhysioNet-multimodal-gait-dataset-1.0.0,

title = {A multimodal gait dataset of brain activity, muscle activity, kinematics and ground forces in young adults},

author = {Rateb Katmah and Aamna AlShehhi and Doua Kosaji and Nour Al-Rahmani and Muhammad Abdullah and Abdul Aziz Vaqar Hulleck and Kinda Khalaf},

url = {https://doi.org/10.13026/r0ea-7161},

doi = {10.13026/r0ea-7161},

year  = {2026},

date = {2026-04-30},

urldate = {2026-04-01},

journal = {PhysioNet},

abstract = {Gait is a fundamental motor function, and its analysis is essential for understanding locomotor control, rehabilitation, and the early detection of neurological and musculoskeletal disorders. While many datasets capture either biomechanical or neural aspects of gait, publicly available multimodal datasets that integrate brain, muscle, kinematic, and ground reaction force recordings remain scarce. This limitation restricts advances in modeling neuromechanical interactions and the development of machine learning approaches for gait classification and rehabilitation technologies. To address this gap, we provide a comprehensive dataset of treadmill walking from 59 healthy adults with a mean age of 24 ± 5 years, representing both sexes and different body mass index categories. Participants walked at three controlled speeds (0.5, 0.75, and 1.0 m/s), with synchronized recordings from scalp electroencephalography, surface electromyography of 12 lower-limb muscles, inertial sensors capturing kinematics, and bilateral force plates measuring three-dimensional forces, moments, and center of pressure. The dataset enables investigations into brain-body interactions, speed-dependent adaptations, and neuromechanical variability, while supporting the benchmarking of computational models for gait analysis.



},

note = {Version 1.0.0},

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pubstate = {published},

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}