DSI-24

@article{lee2025feasibility,

title = {Feasibility Study on Microstate Analysis to Enhance Generalizability in EEG Research of Construction Workers’ Cognitive States},

author = {Meesung Lee and Eren Shahrokhi and Syed Nizam Ahmed and Gaang Lee},

doi = {https://doi.org/10.1061/9780784486436.104},

year  = {2026},

date = {2026-01-28},

booktitle = {Computing in Civil Engineering 2025: Computational and Intelligent Technologies},

journal = {Computing in Civil Engineering 2025: Computational and Intelligent Technologies},

pages = {971–979},

abstract = {The safety and productivity of construction projects are closely tied to workers’ cognitive states. Traditional methods for assessing these states often suffer from self-report bias and lack dynamic tracking. Electroencephalogram (EEG) provides an objective, real-time alternative, but its utility is limited by poor generalizability due to inter-individual variability. EEG microstate analysis, which converts EEG signals into cross-population brain network patterns, may buffer these differences and improve model performance. This study investigates the feasibility of using EEG microstates to enhance the generalizability of cognitive state classification among construction workers. EEG data labeled as desirable or undesirable cognitive states were collected during cognitively demanding tasks. Deep learning models were trained with and without EEG microstates using leave-one-subject-out validation. Results show that integrating microstates consistently improved classification accuracy, even with identical data volumes. These findings highlight the value of microstates in capturing individual variability and advancing EEG-based cognitive monitoring in construction settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{olikkalsivam,

title = {SIVAM: Synergy-Based Intuitive Virtual and Augmented Therapy for Mental Health},

author = {Parthan Olikkal and Oritsejolomisan Mebaghanje and Viraj Janeja and Golnaz Moharrer and Akshara Ajendla and Sruthi Sundharram and Andrea Kleinsmith and Ann Sofie Clemmensen and Ramana Vinjamuri},

url = {https://link.springer.com/chapter/10.1007/978-3-032-06713-5_17},

year  = {2026},

date = {2026-01-20},

journal = {Bridging the Gap between Mind and Machine},

pages = {343},

publisher = {Springer},

abstract = {With growing mental health challenges among populations and limited access to in-person therapy, there is a critical need for accessible, personalized, and non-pharmacological interventions. To address this, we developed the Synergy-based Intuitive Virtual and Augmented Therapy for Mental Health (SIVAM) platform, a real-time home-deployable system that delivers emotionally responsive dance movement therapy (DMT). SIVAM integrates full-body and hand motion capture, avatar-based interaction, and humanoid robot mirroring, while simultaneously recording multimodal physiological signals (EEG, EMG, ECG, GSR, temperature). By extracting motor synergies and affective biomarkers, the system aims to adapt choreography and feedback to the user’s emotional and physical state. The platform demonstrates low-latency communication, high-fidelity mapping of body landmarks to avatars and robots, and seamless synchronization between movement and biofeedback. A pilot study confirmed SIVAM’s effectiveness across user profiles, supporting its potential as an emotion-aware, scalable therapeutic solution tailored to the need of individuals.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chen2026prolonged,

title = {Prolonged Mismatch Negativity Latencies in Intensive Care Unit Patients With Active Delirium},

author = {Yi-Cian Chen and Huai-Jen Chang and Ching-Wen Chang and Jia-Ling Li and Hsinjie Lu and Chia-Hsiung Cheng},

url = {https://www.ovid.com/jnls/clinicalneurophys/abstract/10.1097/wnp.0000000000001238~prolonged-mismatch-negativity-latencies-in-intensive-care},

year  = {2026},

date = {2026-01-16},

urldate = {2026-01-01},

journal = {Journal of Clinical Neurophysiology},

abstract = {Introduction:

Delirium is a common and serious complication in critically ill patients, associated with higher mortality, prolonged intensive care unit (ICU) stays, and cognitive impairments. Furthermore, renal dysfunction is a well-recognized risk factor for delirium in the ICU. Although previous studies have explored the neurophysiologic characteristics of delirium, few have examined brain activity during active delirium episodes. To address this gap, this study aimed to use mismatch negativity (MMN)—an electrophysiologic marker of the brain's automatic ability to detect environmental changes—to deepen the understanding of the pathophysiology and phenomenology of delirium in ICU patients with renal dysfunction.



Methods:

An auditory oddball paradigm, consisting of frequent standard tones and infrequent deviant tones, was presented to critically ill patients with renal dysfunction during event-related potential recordings. MMN was obtained by subtracting the event-related potential response to deviant stimuli from that of standard stimuli and was compared between patients with and without delirium. In addition, the authors examined the relationships between MMN, cognitive function, and disease severity. Finally, they assessed whether MMN could predict key clinical outcomes at ICU discharge.



Results:

ICU patients with delirium exhibited significantly prolonged MMN latencies compared with those without delirium (P = 0.005, effect size = 0.67). Moreover, more delayed MMN latencies showed a trend toward an association with greater delirium severity. However, MMN did not predict key clinical outcomes on ICU discharge.



Conclusions:

Critically ill patients with renal dysfunction exhibit prolonged MMN latencies during delirium episodes compared with those without delirium, suggesting altered neural processing in this population.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{gupta2025methodological,

title = {Methodological optimization for eliciting robust median nerve somatosensory evoked potentials for realtime single trial applications},

author = {Disha Gupta and Jodi Ann Brangaccio and NJ Hill},

doi = {10.1088/1741-2552/ae30ac},

year  = {2025},

date = {2025-12-23},

urldate = {2025-01-01},

journal = {Journal of Neural Engineering},

abstract = {Objective: Single-trial measurement of median nerve Somatosensory Evoked Potentials (SEPs) with noninvasive electroencephalography (EEG) is challenging due to low signal-to-noise ratio (SNR), limiting its use in real-time neurorehabilitation applications. We describe and evaluate methodological optimizations for eliciting reliable median nerve SEPs measurable in real time, with reduced reliance on post-processing.



Methods: In twelve healthy participants, two sessions each, SEPs were assessed at three pulse widths (0.1, 0.5, 1 msec), at a low-frequency stimulation (0.5 Hz ± 10%), and at an intensity sufficient to evoke consistent and robust sensory nerve action potentials (SNAPs) and compound muscle action potentials (CMAPs). The Evoked Potential Operant Conditioning System platform was used to monitor responses in real time. Feasibility was also evaluated in a participant with incomplete spinal cord injury (iSCI).



Results: SEP P50 and N70 were reliably elicited in healthy participants, and in individual with iSCI, across all tested pulse widths with minimal discomfort. N70 amplitude increased significantly with pulse width (χ2= 17.64, p= 0.0001, w= 0.80), while P50 amplitude remained unchanged. SNR showed a significant pulse width-dependent increase (χ2= 7.82, p= 0.02, w= 0.35) with improvements of 40% and 52% at 0.5 and 1 msec, respectively. N70 single-trial separability significantly improved at 1 msec (AUC of 0.83, χ2= 8.17, p= 0.017), including the iSCI participant (0.84-less impaired hand, 0.79-more impaired hand). Test-retest reliability (ICC= 0.70-0.84, p< 0.05) was highest at 0.5 msec, indicating more consistent N70 and P50 measurements across sessions at a longer pulse width.



Significance: Robust median nerve SEPs can be measured at single trials with methodological optimizations such as a longer pulse width (0.5-1ms), low frequency (0.5 Hz), a consistent afferent excitation guided by nerve and muscle responses, and a robust EEG acquisition system. This setup can be useful for real time SEP-based brain computer interface applications for rehabilitation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{wang2025gamma,

title = {Gamma entrainment modulates cognitive impaired subgroup of depression: Evidence from EEG microstate dynamics},

author = {Hongli Wang and Xiaoning Shi and Yongsheng Qi and Michel Gao and Yingying Zhao},

doi = {https://doi.org/10.1016/j.jad.2025.120971},

year  = {2025},

date = {2025-12-19},

urldate = {2025-01-01},

journal = {Journal of Affective Disorders},

pages = {120971},

publisher = {Elsevier},

abstract = {Aim

Depression characterized by heterogeneous symptom profiles, has increasingly been recognized for its cognitive subtype. Gamma entrainment achieved through rhythmic sensory stimulation, has emerged as a promising method to restore neural synchrony. However, the effects and mechanisms of gamma entrainment to cognitive impairment in depression remain poorly understood.



Method

We identified the cognitive impairment subgroup in depression by MATRICS Consensus Cognitive Battery. And then, we randomly classified these cognitively impaired patients into intervention and control group, gamma entrainment training was used. Microstates of neuronal oscillations were conducted through scalp electroencephalogram.



Results

Microstate dynamics during eyes-closed conditions in the cognitive biotype of depression revealed distinct alterations across specific neural oscillatory parameters. Significant reductions were observed in Class-B global field power (GFP) and Class-D metrics, including GFP, duration, and coverage. Conversely, Class-C Occurrence frequency (Occ) exhibited increased activation in the cognitive biotype. Transition probabilities between Class B and D were also attenuated. After gamma entrainment， cognitive function improved in cognitive biotype of depression without affecting emotional symptoms. Moreover, significant temporal main effects emerged for Class-B microstate dynamics, including increase in global field power, duration, and coverage, alongside Class-D GFP.



Conclusion

Our findings delineate a distinct neurophysiological signature of the cognitive biotype of depression, marked by bidirectional dysregulation of large-scale neural synchrony. These results position gamma-driven neuromodulation as a potential therapeutic strategy for restoring network synchrony in cognitive-biased depression, while highlighting microstate metrics as sensitive biomarkers for biotype stratification. Future studies should validate these dynamics in longitudinal cohorts and assess their predictive value for treatment responsiveness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{zhao2025parietal,

title = {Parietal gamma oscillations decreased in cognitive biotype of depression},

author = {Yingying Zhao and Xiaoning Shi and Ruinan Li and Chenyang Wang and Yongsheng Qi and Shawn Lihao Dai and Liao Li and Michel Gao and Hongli Wang},

url = {https://www.nature.com/articles/s41598-025-20977-9},

year  = {2025},

date = {2025-10-23},

urldate = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

number = {1},

pages = {37100},

publisher = {Nature Publishing Group UK London},

abstract = {Cognitive biotype in depression has long been associated with abnormalities in neural oscillations. Among them, gamma oscillations are widely observed correlates of cognitive dysfunction. However, whether gamma oscillations implement causal mechanisms of specific brain function in cognitive biotype of depression remains unclear. Depressed patients in remission were included in this study. Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) was used to identify cognitive biotype. Here, we enrolled 141 individuals with stable depression, 56 were divided into cognitive impairment (CI) biotype according to MCCB scores. And gamma neural oscillations in resting-state were recorded through electroencephalography (EEG). In the eyes-closed condition, CI biotype showed decreased low-gamma power in P3 channel (t =-3.267, FDR = 0.026) except other channels. And there was no statistical difference in low-gamma and high-gamma power in Fp, F, C, T, P, O between CI and NCI biotype in depression. Moreover, statistically correlations between cognitive function and gamma power were observed. In the eyes-closed condition, low-gamma oscillation was correlated with working memory (r = 0.205, P = 0.015). Also, in the eyes-open condition, low- and high-gamma oscillation was correlated with social cognition (r = -0.175, P = 0.038; r = -0.241, P = 0.004). Our results confirmed that gamma neural oscillations decreased in cognitive biotype of depression. The findings also demonstrate a preliminary correlation between gamma-band oscillations and working memory, suggesting that gamma activity may serve as a neural substrate for efficient information processing during cognitive tasks. This reinforces the theoretical framework implicating gamma synchrony in higher-order brain functions and highlights its potential as a biomarker for cognitive assessment.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kalaganis2025hybrid,

title = {A hybrid neuromarketing approach exploiting EEG graph signal processing and gaze dynamic patterning},

author = {Fotis P Kalaganis and Kostas Georgiadis and Vangelis P Oikonomou and Nikos A Laskaris and Spiros Nikolopoulos and Ioannis Kompatsiaris},

doi = {https://doi.org/10.1186/s40708-025-00272-z},

year  = {2025},

date = {2025-09-23},

urldate = {2025-01-01},

journal = {Brain Informatics},

volume = {12},

number = {1},

pages = {23},

publisher = {Springer},

abstract = {In this study, we propose a hybrid decoding scheme for classifying consumer intent in a binary decision-making scenario (“Buy” vs. “NoBuy”), using simultaneous electroencephalography (EEG) and eye-tracking data. The proposed framework integrates graph signal processing-based features derived from EEG functional connectivity with descriptive statistics from eye movement patterns. Given the imbalanced nature of the targeted classification task, the performance of the proposed hybrid scheme is being assessed at the individual subject level via the employment of Cohen’s kappa and F1-score metrics, both of which are well-suited for handling class imbalance by accounting for agreement beyond chance and balancing precision and recall, respectively. The reported results showcase the superiority of the proposed hybrid decoding scheme, as the averaged scores for both Cohen’s kappa and F1-score are exceeding (with statistical significance at 0.05) the presented competing approaches by 0.08–0.30 and 0.06–0.23 respectively. Additionally, our connectivity analysis confirmed two key findings: (i) strong couplings were consistently observed between electrodes spanning distinct brain regions, such as the prefrontal and occipital cortices, in addition to the commonly reported frontal dipoles; and (ii) the most salient functional connections varied across individuals, with only a limited subset shared among subjects. These results highlight the potential of multimodal decoding approaches and subject-specific connectivity patterns in advancing the classification of consumer decision behavior.



},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cho2025eeg,

title = {EEG-based evaluation of illuminance and correlated color temperature adjusted to fatigue levels in a VR residential environment},

author = {Younjoo Cho and Dabin Lee and Anseop Choi and Minki Sung},

doi = {https://doi.org/10.1016/j.buildenv.2025.113702},

year  = {2025},

date = {2025-09-19},

urldate = {2025-01-01},

journal = {Building and Environment},

pages = {113702},

publisher = {Elsevier},

abstract = {Fatigue directly affects quality of life, but its causes and treatments remain unclear, and managing it with sufficient rest is essential. This study evaluates fatigue-based lighting (FBL), a system that adjusts illuminance and correlated color temperature (CCT) to alleviate fatigue. In a virtual reality (VR) one-room apartment, electroencephalogram (EEG) signals were measured from 24 healthy adults (11 men, 13 women; aged 24–43 years), yielding 28 valid datasets. Participants were first exposed to a standard lighting condition (300 lx, 6000 K). After a short rest, they were exposed to one of three FBL conditions based on their fatigue level: level A (“a little tired”: 400 lx, 5000 K), level B (“tired”: 500 lx, 4000 K), or level C (“very tired”: 150 lx, 3000 K). EEG changes before and after exposure were analyzed using the Alpha/Beta Ratio (ABR), an indicator of relaxation. The mean ABR increased at 17 of 19 electrodes, suggesting a positive effect on fatigue relief. Significant increases were found at five electrodes (FP1, FP2, F7, T4, O1), corresponding to the frontopolar, frontal, temporal, and occipital regions. Further analysis showed that ABR values at FP1, F7, and T4 increased significantly (p < 0.05) under fatigue level B, implying that lighting at 500 lx and 4000 K may have the potential to alleviate fatigue at this level. These findings provide neurophysiological evidence that lighting tailored to fatigue level can promote recovery and highlight the potential of personalized, adaptive lighting systems for human-centric design in residential and occupational environments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}