@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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}

@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.},

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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},

keywords = {},

pubstate = {published},

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}

@article{wang2026uav,

title = {UAV Target Detection and Tracking Integrating a Dynamic Brain–Computer Interface},

author = {Jun Wang and Zanyang Li and Lirong Yan and Muhammad Imtiaz and Hang Li and Muhammad Usman Shoukat and Jianatihan Jinsihan and Benjun Feng and Yi Yang and Fuwu Yan and others},

doi = {https://doi.org/10.3390/drones10030222},

year  = {2026},

date = {2026-03-20},

urldate = {2026-01-01},

journal = {Drones},

volume = {10},

number = {3},

pages = {222},

publisher = {MDPI},

abstract = {To address the inherent limitations in the robustness of fully autonomous unmanned aerial vehicle (UAV) visual perception and the high cognitive workload associated with manual control, this paper proposes a human-in-the-loop brain–computer interface (BCI) control framework. The system integrates steady-state visual evoked potential (SSVEP) with deep learning techniques to create a spatio-temporally dynamic interaction paradigm, enabling real-time alignment between visual targets and frequency stimuli. At the perception level, an enhanced YOLOv11 network incorporating partial convolution (PConv) and shape intersection over union (Shape-IoU) loss is developed and coupled with the DeepSort multi-object tracking algorithm. This configuration ensures high-speed execution on edge computing platforms while maintaining stable stimulus coverage over dynamic targets, thus providing a robust visual induction environment for EEG decoding. At the neural decoding level, an enhanced task-discriminant component analysis (TDCA-V) algorithm is introduced to improve signal detection stability within non-stationary flight conditions. Experimental results demonstrate that within the predefined fixation task window, the system achieves 100% success in maintaining target identity (ID). The BCI system achieved an average command recognition accuracy of 91.48% within a 1.0 s time window, with the TDCA-V algorithm significantly outperforming traditional spatial filtering methods in dynamic scenarios. These findings demonstrate the system’s effectiveness in decoupling human cognitive intent from machine execution, providing a robust solution for human–machine collaborative control.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ditemporal,

title = {Temporal Dynamics of Functional Connectivity during Neuroaesthetic Experience: Effects of Art Expertise and Psychological Traits},

author = {Accademia di Belle Arti Napoli},

url = {https://www.researchgate.net/profile/Pietro-Tarchi/publication/401637294_Temporal_Dynamics_of_Functional_Connectivity_During_Neuroaesthetic_Experience_Effects_of_Art_Expertise_and_Psychological_Traits/links/69aeb209ceb31f79ab25270a/Temporal-Dynamics-of-Functional-Connectivity-During-Neuroaesthetic-Experience-Effects-of-Art-Expertise-and-Psychological-Traits.pdf},

year  = {2026},

date = {2026-03-14},

abstract = {In recent years, neuroaesthetics has highlighted how the experience of art engages brain networks related to perception, cognition, and emotion. In this study, electroencephalography (EEG) was recorded from 35 participants (22.86 ± 1.96 years) while they viewed 70 artworks. Functional connectivity was estimated with the Phase Lag Index across theta, alpha, beta, and gamma bands, and graph-theoretical metrics were derived. Analyses were conducted separately for early (0-1 s) and later (1-2 s) post-stimulus epochs, comparing experts and non-experts, as well as high- and low-empathy individuals (defined based on Interpersonal Reactivity Index scores). Results revealed that expertise exerted its strongest influence during the first second of processing, characterized by reduced theta transitivity and betweenness and increased alpha participation, particularly for familiar artworks. In the second epoch, expertise effects became more selective, with differences in beta and gamma metrics. Empathy effects, by contrast, were weaker in the first second, whereas in the second epoch empathic individuals exhibited more robust modulations, including higher path length, modularity, and altered clustering and centrality across alpha, beta, and gamma bands for high-valence and high-arousal artworks. These findings suggest that art expertise predominantly shapes early, familiarity-driven connectivity, whereas empathy modulates later affective stages of processing.

},

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pubstate = {published},

tppubtype = {article}

}

@article{seo2026development,

title = {Development of Drowsy Driving Detection System Using EEG},

author = {Ssang-Hee Seo},

doi = {https://doi.org/10.31803/tg-20250326030355},

year  = {2026},

date = {2026-03-13},

urldate = {2026-01-01},

journal = {Tehnički glasnik},

volume = {20},

number = {1},

pages = {86–93},

publisher = {Sveučilište Sjever},

abstract = {Drowsy driving is a major contributor to serious traffic accidents, highlighting the urgent need for effective real-time detection systems. This study proposes a real-time drowsiness detection system based on electroencephalogram (EEG) signals and a lightweight convolutional neural network (CNN). The system comprises five main components: EEG signal acquisition, preprocessing, feature extraction, CNN-based classification, and user feedback delivery via an Android application. The experiment involved four healthy adult male participants with an average age of 24.5 years. EEG data were collected using the DSI-24 device, and the relative power in the alpha band from the prefrontal (Fp1, Fp2) and occipital (O1, O2) regions was identified as the primary feature for distinguishing drowsiness. The proposed CNN model, trained on these features, achieved a classification accuracy of 91.56%, comparable to the 92.66% accuracy of the more complex AlexNet model, while being significantly more lightweight and suitable for real-time deployment on embedded systems. The Android application provides real-time feedback on the user’s drowsiness level and recommends nearby rest areas to help mitigate the risk of drowsy driving. This study presents a practical and efficient EEG-based driver monitoring solution. Future work will focus on large-scale data collection under actual driving conditions to further validate and improve the system’s performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{xu2026f2fnet,

title = {F2FNet: An Efficient Affective State Analysis Network Reconstructing EEG From Few-Channel To Full-Channel},

author = {Zihua Xu and Tianjun Li and CL Philip Chen and Tong Zhang},

doi = {10.1109/TAFFC.2026.3671843},

year  = {2026},

date = {2026-03-09},

urldate = {2026-01-01},

journal = {IEEE Transactions on Affective Computing},

publisher = {IEEE},

abstract = {Due to the prohibitive costs and lack of portability associated with full-channel devices, portable miniature EEG devices with only a few electrodes (few-channel) are more suitable for widespread deployment in consumer applications. However, affective state analysis with few-channel EEG presents a significant challenge, as these devices only capture signals from partial brain regions. Existing approaches designed for few-channel devices do not adequately account for the critical role of whole-brain connectivity and suffer from the impact of noise in affective state analysis. To address the challenges, we propose an efficient affective state analysis Network which reconstructs EEG from Few-channel To Full-channel (F2FNet). This method aims to leverage knowledge from full-channel EEG and exclude irrelevant information to enhance the affective state analysis performance of few-channel EEG based on whole-brain connectivity patterns. To ensure information validity and consistency during the reconstruction process, we introduce mechanisms for information compression and control. Additionally, we perform alignment of compressed features within the VAD emotional space to ensure that the model's understanding and extraction of emotional features conform to the definitions of affective theories. Extensive experiments on basic emotion recognition and depression detection demonstrate the efficacy of the proposed method.},

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pubstate = {published},

tppubtype = {article}

}