@article {pmid40553738,
year = {2025},
author = {Zhang, HG and Wang, JF and Jialin, A and Zhao, XY and Wang, C and Deng, W},
title = {Relationship between multimorbidity burden and depressive symptoms in older Chinese adults: A prospective 10-year cohort study.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {119714},
doi = {10.1016/j.jad.2025.119714},
pmid = {40553738},
issn = {1573-2517},
abstract = {BACKGROUND: Recent research indicates that multimorbidity clusters due to common mechanisms and risk factors, leading to different effects on the development of depressive symptoms (DS) in older populations. This study innovatively examined the association of both the number and specific patterns of multimorbidity with DS.

METHODS: A total of 1988 participants aged 60 years and older were selected from the China Health and Retirement Longitudinal Study (CHARLS) and monitored for DS between June 2011 and September 2020. Twelve chronic conditions were assessed through self-reports. DS was evaluated using the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Latent class analysis (LCA) was used to identify multimorbidity patterns, and Cox proportional hazards regression models examined the associations of specific diseases, multimorbidity count and multimorbidity patterns with DS.

RESULTS: During the 9.17-year follow-up period, 986 cases of DS were identified. Hypertension (adjusted hazard ratio [HR] = 1.21, 95 % confidence interval [CI] = 1.05-1.39), stroke (HR = 1.77, 95%CI = 1.20-2.63), stomach or other digestive disease (HR = 1.28, 95%CI = 1.11-1.48), arthritis or rheumatism (HR = 1.41, 95%CI = 1.24-1.60), chronic lung diseases (HR = 1.25, 95%CI = 1.03-1.52) and kidney disease (HR = 1.38, 95%CI = 1.07-1.78) were significantly associated with increased DS risk. Each additional chronic condition increased the DS hazard by 13 % (adjusted HR = 1.13, 95 % CI = 1.08-1.18). Four multimorbidity patterns were identified by LCA, with the digestion/arthritis pattern (HR = 1.47, 95 % CI = 1.22-1.77) and respiratory pattern (HR = 1.47, 95 % CI = 1.07-2.04) showing higher DS risk compared to the relatively healthy group.

CONCLUSION: The number and patterns of multimorbidity are significantly associated with heightened DS risk in older populations. Older adults in complex health conditions, particularly those with digestion/arthritis and respiratory multimorbidity patterns, should receive closer mental health monitoring.},
}

@article {pmid40551292,
year = {2025},
author = {Chu, J and Yao, J and Li, Z and Li, J and Zhang, Y and Liu, C and He, H and Li, B and Wei, H},
title = {Brain tissue electrical conductivity as a promising biomarker for dementia assessment using MRI.},
journal = {Alzheimer's & dementia : the journal of the Alzheimer's Association},
volume = {21},
number = {6},
pages = {e70270},
pmid = {40551292},
issn = {1552-5279},
support = {2024YFC2421100//National Key Research and Development Program of China/ ; //National Natural Science Foundation of China/ ; //62471296, 82271441, 62071299, 82372036, 82001342/ ; 23TS1400200//Shanghai Science and Technology Development Funds/ ; STAR 20220103 YG2023LC02//SJTU Trans-med Awards Research/ ; },
mesh = {Humans ; *Magnetic Resonance Imaging/methods ; Biomarkers ; *Brain/diagnostic imaging/metabolism/physiopathology ; *Dementia/diagnostic imaging/diagnosis/metabolism ; Male ; Female ; Amyloid beta-Peptides/metabolism ; *Electric Conductivity ; tau Proteins/metabolism ; Aged ; Cognitive Dysfunction ; Positron-Emission Tomography ; },
abstract = {INTRODUCTION: Dementia, particularly Alzheimer's disease, involves cognitive decline linked to amyloid beta (Aβ) and tau protein aggregation. Magnetic resonance imaging (MRI)-based brain tissue conductivity, which increases in dementia, may serve as a non-invasive biomarker for protein aggregation. We investigate the relationship between MRI-based brain electrical conductivity, protein aggregation, cognition, and gene expression.

METHODS: Brain conductivity maps were reconstructed and correlated with PET protein signals, cognitive performance, and plasma protein levels. The diagnostic potential of conductivity for dementia was assessed, and transcriptomic analysis using the Allen Human Brain Atlas elucidated the underlying biological processes.

RESULTS: Increased brain conductivity was associated with Aβ and tau aggregation in specific brain regions, cognitive decline, and plasma protein levels. Conductivity also improved dementia discrimination performance, and higher gene expression related to ion transport, cellular development, and signaling pathways was observed.

DISCUSSION: Brain electrical conductivity shows promise as a biomarker for dementia, correlating with protein aggregation and relevant cellular processes.

HIGHLIGHTS: Brain tissue conductivity correlates with Aβ and tau aggregation in dementia. Brain tissue conductivity correlates with cognitive scores and GMV. CSF conductivity correlates with plasma protein levels. Combining conductivity with GMV improves dementia diagnosis accuracy. Gene expression in ion processes, cell development, and signaling links to conductivity.},
}

Humans
*Magnetic Resonance Imaging/methods
Biomarkers
*Brain/diagnostic imaging/metabolism/physiopathology
*Dementia/diagnostic imaging/diagnosis/metabolism
Male
Female
Amyloid beta-Peptides/metabolism
*Electric Conductivity
tau Proteins/metabolism
Aged
Cognitive Dysfunction
Positron-Emission Tomography

@article {pmid40550006,
year = {2025},
author = {Liu, Y and Fan, P and Pan, Y and Ping, J},
title = {Flexible Microinterventional Sensors for Advanced Biosignal Monitoring.},
journal = {Chemical reviews},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.chemrev.5c00115},
pmid = {40550006},
issn = {1520-6890},
abstract = {Flexible microinterventional sensors represent a transformative technology that enables the minimal intervention required to access and monitor complex biosignals (e.g., bioelectrical, biophysical, and biochemical signals) originating from deep tissues, thereby providing accurate data for diagnostics, robotics, prosthetics, brain-computer interfaces, and therapeutic systems. However, fully unlocking their potential hinges on establishing a nondisruptive, intimate, and nonrestrictive interface with the tissue surface, facilitating efficient integration between the microinterventional sensor and the target tissue. In this comprehensive review, we highlight the critical tissue characteristics in both physiologically and pathologically relevant contexts that are pivotal for the design of microinterventional sensors. We also summarize recent advancements in flexible substrate materials and conductive materials, which are tailored to facilitate effective information interaction between bioelectronic components and biological tissues. Furthermore, we classify various electrode architectures─spanning 1D, 2D, and 3D─designed to accommodate the mechanics of soft tissues and enable nonrestrictive interfaces in diverse sensing scenarios. Additionally, we outline critical challenges for next-generation microinterventional sensors and propose integrating advanced materials, innovative fabrication, and embedded intelligence to drive breakthroughs in biosignal sensing. Ultimately, we aim to both provide foundational understanding and highlight emerging strategies in biosignal capture, leveraging recent advancements in these critical components.},
}

@article {pmid40549688,
year = {2025},
author = {Meijs, S and Andreis, FR and Kjærgaard, B and Janjua, TAM and Jensen, W},
title = {Chronic Cranial Window Technique for Repeated Cortical Recordings During Anesthesia in Pigs.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {220},
pages = {},
doi = {10.3791/67931},
pmid = {40549688},
issn = {1940-087X},
mesh = {Animals ; Swine ; *Electrocorticography/methods/instrumentation ; *Anesthesia/methods ; *Somatosensory Cortex/physiology ; Dura Mater/surgery ; Electrodes, Implanted ; },
abstract = {Cortical recordings are essential for extracting neuronal signals to inform various applications, including brain-computer interfaces and disease diagnostics. Each application places specific requirements on the recording technique, and invasive solutions are often selected for long-term recordings. However, invasive recording methods are challenged by device failure and adverse tissue responses, which compromise long-term signal quality. To improve the reliability and quality of chronic cortical recordings while minimizing risks related to device failure and tissue reactions, we developed a cranial window technique. In this protocol, we report methods to implant and access a cranial window in juvenile landrace pigs, which facilitates temporary electrocorticography (ECoG) array placement on the dura mater. We further describe how cortical signals can be recorded using the cranial window technique. Cranial window access can be repeated several times, but a minimum of 2 weeks between implant and access surgeries is advised to facilitate recovery and tissue healing. The cranial window approach successfully minimized common electrode failure modes and tissue responses, resulting in stable and reliable cortical recordings over time. We recorded event-related potentials (ERPs) from the primary somatosensory cortex as an example. The method provided highly reliable recordings, which also allowed the assessment of the effect of an intervention (high-frequency stimulation) on the ERPs. The absence of significant device failures and the reduced number of electrodes used (two electrodes, 43 recording sessions, 16 animals) suggest an improved research economy. While minor surgical access is required for electrode placement, the method offers advantages such as reduced infection risk and improved animal welfare. This study presents a scalable, reliable, and reproducible method for chronic cortical recordings, with potential applications in various fields of neuroscience, including pain research and neurological disease diagnosis. Future adaptations may extend its use to other species and recording modalities, such as intracortical recordings and imaging techniques.},
}

Animals
Swine
*Electrocorticography/methods/instrumentation
*Anesthesia/methods
*Somatosensory Cortex/physiology
Dura Mater/surgery
Electrodes, Implanted

@article {pmid40549518,
year = {2025},
author = {Yu, F and Rao, Z and Chen, N and Liu, L and Jiang, M},
title = {ArmBCIsys: Robot Arm BCI System With Time-Frequency Network for Multiobject Grasping.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3579332},
pmid = {40549518},
issn = {2162-2388},
abstract = {Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module. The proposed DBFENet designs the scaling temporal convolution block (STCB) to extract multiscale spatiotemporal features from the time domain, while the designed DropScale projected Transformer (DSPT) utilizes discrete cosine transform (DCT) to capture key frequency-domain features, significantly improving decoding robustness. We fine-tune the masked-attention mask Transformer (Mask2Former) model on the Jacquard dataset and incorporate the multiframe centroid-intersection over union (IoU) tracking algorithm to build visual grasp segmenter (VisGraspSeg), enabling reliable segmentation and dynamic tracking for diverse daily objects. Experimental validations on both self-built code-modulated visual evoked potential (c-VEP) dataset (1344 samples) and two public c-VEP datasets demonstrate that DBFENet achieves the state-of-the-art recognition performance, and the system integrates the DBFENet and proposed vision-guided module and ensures stable multiobject selecting and automatic object grasping in dynamic environments, extending promising applications in healthcare robotics, assistive technology, and industrial automation. The self-built dataset has been made publicly accessible at https://github.com/wtu1020/ ArmBCIsys-Self-built-cVEP-Dataset.},
}

@article {pmid40548156,
year = {2025},
author = {Tzimourta, KD},
title = {Human-Centered Design and Development in Digital Health: Approaches, Challenges, and Emerging Trends.},
journal = {Cureus},
volume = {17},
number = {6},
pages = {e85897},
pmid = {40548156},
issn = {2168-8184},
abstract = {Human-centered design (HCD) has emerged as a critical approach for developing digital health technologies that are usable, acceptable, and effective within complex healthcare environments. Rooted in systems theory, ergonomics, and information systems research, HCD prioritizes the needs, capabilities, and limitations of diverse user groups - including patients, clinicians, and caregivers - throughout the design and implementation process. This review synthesizes current applications of HCD in four key domains: brain-computer interfaces (BCIs), augmented and virtual reality (AR/VR), artificial intelligence (AI)-based clinical decision support systems AI-CDSS, and mobile health (mHealth) technologies. It explores design frameworks, usability strategies, and models of human-technology collaboration that contribute to successful adoption and sustained use. Ethical and legal considerations - such as data privacy, informed consent, and algorithmic fairness - are also addressed, particularly in contexts involving biometric and neurophysiological data. While HCD practices have shown substantial potential to improve digital health outcomes, their implementation remains uneven across technologies and settings. Emerging trends, including adaptive personalization, explainable AI, and participatory co-design, are identified as promising directions for the development of more inclusive, trustworthy, and sustainable digital health innovations.},
}

@article {pmid40546334,
year = {2025},
author = {Cruz, MV and Jamal, S and Sethuraman, SC},
title = {A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.},
journal = {Journal of medical signals and sensors},
volume = {15},
number = {},
pages = {16},
pmid = {40546334},
issn = {2228-7477},
abstract = {The brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in health care. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in health care and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.},
}

@article {pmid40545006,
year = {2025},
author = {Feng, J and Jia, W and Li, Z},
title = {Electroencephalography: A Valuable Tool for Assessing Motor Impairment and Recovery Post-Stroke.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110518},
doi = {10.1016/j.jneumeth.2025.110518},
pmid = {40545006},
issn = {1872-678X},
abstract = {Stroke is a leading cause of adult disability, and restoring motor function post-stroke is critical to improving the well-being and quality of life of affected individuals. Accurate and timely assessment of motor function is essential for developing effective rehabilitation strategies and predicting recovery outcomes. Electroencephalography (EEG) offers a non-invasive, real-time monitoring of brain activity, offering personalized insights into motor impairment and recovery. Its simplicity and bedside applicability make EEG a valuable tool and a potential biomarker for brain function. In recent years, the integration of EEG with the brain-computer interface technology and neuromodulation techniques has revolutionized personalized rehabilitation therapy, offering new hope for patients with motor dysfunction following stroke. This review synthesizes evidence on EEG-derived biomarkers and their integration with brain-computer interface and neuromodulation techniques, proposing a framework for personalized rehabilitation strategies in stroke recovery.},
}

@article {pmid40544658,
year = {2025},
author = {Mathon, B and Navarro, V and Pons, T and Lecas, S and Roussel, D and Charpier, S and Carpentier, A},
title = {Ultrasound-induced blood-brain barrier opening and selenium-nanoparticle injection lower seizure activity: A mouse model of temporal lobe epilepsy.},
journal = {Ultrasonics},
volume = {155},
number = {},
pages = {107734},
doi = {10.1016/j.ultras.2025.107734},
pmid = {40544658},
issn = {1874-9968},
abstract = {BACKGROUND: Given the limitations of current treatment options for drug-resistant mesial temporal lobe epilepsy (MTLE), the development of novel, nonablative and minimally invasive surgical techniques is essential.

OBJECTIVE AND METHODS: In this study, low-intensity pulsed ultrasound (LIPU)- and microbubble-induced (henceforth LIPU) blood-brain barrier (BBB) opening combined with selenium-nanoparticle (SeNP) intravenous injection in a mouse model of mesial temporal lobe optimized the latter's bioavailability in the brain epileptic tissue of the kainic acid (KA) mouse model of MTLE. We aimed to assess the safety and antiepileptic potential of LIPU-enhanced SeNP delivery against KA-induced seizures using long-term intracranial electroencephalogram video recordings and evaluating neuroinflammation, astrogliosis, neuronal apoptosis and neurogenesis in the hippocampal tissues of mice.

RESULTS: First, we established that SeNP intravenous injection combined with LIPU-induced BBB disruption was the most effective method to achieve high and sustained selenium levels in the brain. The safety of this treatment was demonstrated after three treatment sessions, 1-week apart, with no adverse effects observed. Our results further showed a significantly lower frequency of epileptic seizures (-90 %, P = 0.001) in KA mice treated with LIPU + SeNPs compared to sham-treated controls. Short- and long-term histological changes were seen after that combined regimen, including less aberrant neurogenesis in the hippocampus hilum, less neuronal death throughout the hippocampus and less hippocampal microglial activation, which might collectively contribute to the observed antiseizure effect.

CONCLUSION: SeNP injection combined with LIPU-induced BBB disruption demonstrated potential as a promising approach to reduce seizure activity in MTLE; however, statistical comparison did not conclusively establish superiority over SeNPs alone. Further investigations are necessary to consider translational studies in humans.},
}

@article {pmid40542951,
year = {2025},
author = {Chen, J and Sun, G and Zhang, Y and Chen, W and Zheng, X and Zhang, S and Hao, Y},
title = {Interactively Integrating Reach and Grasp Information in Macaque Premotor Cortex.},
journal = {Neuroscience bulletin},
volume = {},
number = {},
pages = {},
pmid = {40542951},
issn = {1995-8218},
abstract = {Reach-to-grasp movements require integrating information on both object location and grip type, but how these elements are planned and to what extent they interact remains unclear. We designed a new experimental paradigm in which monkeys sequentially received reach and grasp cues with delays, requiring them to retain and integrate both cues to grasp the goal object with appropriate hand gestures. Neural activity in the dorsal premotor cortex (PMd) revealed that reach and grasp were similarly represented yet not independent. Upon receiving the second cue, the PMd continued encoding the first, but over half of the neurons displayed incongruent modulations: enhanced, attenuated, or even reversed. Population-level analysis showed significant changes in encoding structure, forming distinct neural patterns. Leveraging canonical correlation analysis, we identified a shared subspace preserving the initial cue's encoding, contributed by both congruent and incongruent neurons. Together, these findings reveal a novel perspective on the interactive planning of reach and grasp within the PMd, providing insights into potential applications for brain-machine interfaces.},
}

@article {pmid40541755,
year = {2025},
author = {Yang, A and Tian, J and Wang, W and Zhou, L and Zhou, K},
title = {Shared and distinct neural signatures of feature and spatial attention.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121296},
doi = {10.1016/j.neuroimage.2025.121296},
pmid = {40541755},
issn = {1095-9572},
abstract = {The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA.},
}

@article {pmid40502202,
year = {2025},
author = {Srinivasan, A and Wairagkar, M and Iacobacci, C and Hou, X and Card, NS and Jacques, BG and Pritchard, AL and Bechefsky, PH and Hochberg, LR and AuYong, N and Pandarinath, C and Brandman, DM and Stavisky, SD},
title = {Encoding of speech modes and loudness in ventral precentral gyrus.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {40502202},
issn = {2692-8205},
abstract = {The ability to vary the mode and loudness of speech is an important part of the expressive range of human vocal communication. However, the encoding of these behaviors in the ventral precentral gyrus (vPCG) has not been studied at the resolution of neuronal firing rates. We investigated this in two participants who had intracortical microelectrode arrays implanted in their vPCG as part of a speech neuroprosthesis clinical trial. Neuronal firing rates modulated strongly in vPCG as a function of attempted mimed, whispered, normal or loud speech. At the neural ensemble level, mode/loudness and phonemic content were encoded in distinct neural subspaces. Attempted mode/loudness could be decoded from vPCG with an accuracy of 94% and 89% for two participants respectively, and corresponding neural preparatory activity could be detected hundreds of milliseconds before speech onset. We then developed a closed-loop loudness decoder that achieved 94% online accuracy in modulating a brain-to-text speech neuroprosthesis output based on attempted loudness. These findings demonstrate the feasibility of decoding mode and loudness from vPCG, paving the way for speech neuroprostheses capable of synthesizing more expressive speech.},
}

@article {pmid40541523,
year = {2025},
author = {Cao, S and Yin, Y and Li, W and Liu, Z and Chen, Z},
title = {Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks.},
journal = {ISA transactions},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.isatra.2025.06.004},
pmid = {40541523},
issn = {1879-2022},
abstract = {This paper explores the control of time-varying formations in a class of heterogeneous multi-agent systems. The key innovation lies in the simultaneous consideration of hybrid actuator faults and deception attacks. To achieve the control objective, a novel distributed double-layer control scheme, comprising a network layer and a physical layer, is proposed. In the network layer, a distributed observer with secure output feedback control is developed to mitigate severe deception attacks, ensuring that the mean square observer error remains within an acceptable range. In the physical layer, fault compensators are designed to address both additive and multiplicative faults. As a result, the followers achieve time-varying formation control, and closed-loop stability analysis is conducted using the Lyapunov method. Finally, to verify the validity of the theoretical findings, numerical simulations are subsequently conducted.},
}

@article {pmid40538971,
year = {2025},
author = {Miao, Y and Li, K and Zhao, W and Zhang, Y},
title = {EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {94},
pmid = {40538971},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.},
}

@article {pmid40538970,
year = {2025},
author = {Lin, C and Lu, H and Pan, C and Ma, S and Zhang, Z and Tian, R},
title = {MBRSTCformer: a knowledge embedded local-global spatiotemporal transformer for emotion recognition.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {95},
pmid = {40538970},
issn = {1871-4080},
abstract = {Emotion recognition is an essential prerequisite for realizing generalized BCI, which possesses an extensive range of applications in real life. EEG-based emotion recognition has become mainstream due to its real-time mapping of brain emotional activities, so a robust EEG-based emotion recognition model is of great interest. However, most existing deep learning emotion recognition methods treat the EEG signal as a whole feature extraction, which will destroy its local stimulation differences and fail to extract local features of the brain region well. Inspired by the cognitive mechanisms of the brain, we propose the multi-brain regions spatiotemporal collaboration transformer (MBRSTCfromer) framework for EEG-based emotion recognition. First, inspired by the prior knowledge, we propose the Multi-Brain Regions Collaboration Network. The EEG data are processed separately after being divided by brain regions, and stimulation scores are presented to quantify the stimulation produced by different brain regions and feedback on the stimulation degree to the MBRSTCfromer. Second, we propose a Cascade Pyramid Spatial Fusion Temporal Convolution Network for multi-brain regions EEG features fusion. Finally, we conduct comprehensive experiments on two mainstream emotion recognition datasets to validate the effectiveness of our proposed MBRSTCfromer framework. We achieved 98.63 % , 98.15 % , and 98.58 % accuracy on the three dimensions (arousal, valence, and dominance) on the DEAP dataset; and 97.66 % , 97.07 % , and 97.97 % on the DREAMER dataset.},
}

@article {pmid40536865,
year = {2025},
author = {Li, Y and Su, D and Yang, X and Wang, X and Zhao, H and Zhang, J},
title = {From Frequency to Temporal: Three Simple Steps Achieve Lightweight High-Performance Motor Imagery Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3579528},
pmid = {40536865},
issn = {1558-2531},
abstract = {OBJECTIVE: To address the challenges of high data noise and substantial model computational complexity in Electroencephalography (EEG)-based motor imagery decoding, this study aims to develop a decoding method with both high accuracy and low computational cost.

METHODS: First, frequency domain analysis was performed to reveal the frequency modeling patterns of deep learning models. Utilizing prior knowledge from brain science regarding the key frequency bands for motor imagery, we adjusted the convolution kernels and pooling sizes of EEGNet to focus on effective frequency bands. Subsequently, a residual network was introduced to preserve high-frequency detailed features. Finally, temporal convolution modules were used to deeply capture temporal dependencies, significantly enhancing feature discriminability.

RESULTS: Experiments were conducted on the BCI Competition IV 2a and 2b datasets. Our method achieved average classification accuracies of 86.23% and 86.75% respectively, surpassing advanced models like EEG-Conformer and EEG-TransNet. Meanwhile, the Multiply-accumulate operations (MACs) were 27.16M, a reduction of over 50% compared to the comparison models, and the Forward/Backward Pass Size was 14.33MB.

CONCLUSION: By integrating prior knowledge from brain science with deep learning techniques-specifically frequency domain analysis, residual networks, and temporal convolutions-it is possible to effectively improve the accuracy of EEG motor imagery decoding while substantially reducing model computational complexity.

SIGNIFICANCE: This paper employs the simplest and most fundamental techniques in its design, highlighting the critical role of brain science knowledge in model development. The proposed method demonstrates broad application potential.},
}

@article {pmid40536747,
year = {2025},
author = {Fei, SW and Chen, JL and Hu, YB},
title = {A novel time-frequency feature extraction method of EEG signals utilizing fractional synchrosqueezing wavelet transform.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {40536747},
issn = {2662-4737},
abstract = {In order to improve the accuracy of Electroencephalogram (EEG) classification, Fractional Synchrosqueezing Wavelet Transform (FSSWT) is proposed to effectively overcome the contradiction between energy concentration and frequency separation in traditional time-frequency analysis methods. Firstly, the principle of FSSWT is introduced, and the time-frequency transformation equation for FSSWT applied to multi-frequency signals is established. The examples of synthetic signal and EEG signal show that the proposed method can suppress the mode aliasing of MI-EEG significantly while maintaining high resolution characteristics, and the energy concentration and related intermediate indexes perform well. The experimental results show that the proposed FSSWT-EEGDNN-ResNet model achieves an average classification accuracy of 95.17% under the condition of the MI-EEG signals processed by FSSWT of eight subjects, demonstrating the effectiveness of FSSWT in EEG signal feature extraction and classification.},
}

@article {pmid40536356,
year = {2025},
author = {Rizzo, M and Dawson, JD},
title = {AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.},
journal = {Annals of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1002/ana.27225},
pmid = {40536356},
issn = {1531-8249},
support = {//University of Nebraska Foundation/ ; U54GM115458//National Institute of General Medical Sciences/ ; R01AG017177//National Institute on Aging/ ; },
abstract = {Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications. We begin by tracing AI's origins in mathematics, human logic, and brain-inspired neural networks to establish a context for its development. The review highlights AI's growing role in neurological diagnostics and treatment, emphasizing machine learning applications, such as computer vision, brain-machine interfaces, and precision care. By mapping the evolution of AI tools and linking them to neuroscience and human reasoning, we illustrate how AI is reshaping neurological practice and research. We end the review with an overview of model selection in AI and a case scenario illustrating how AI may drive precision neurological care. Part 1 sets the stage for part 2, which will focus on practical applications of AI in real-world scenarios where humans and AI collaborate as joint cognitive systems. Part 3 will examine AI's integration with extensive healthcare and neurology networks, innovative clinical trials, and massive datasets, expanding our vision of AI's global impact on neurology, healthcare systems, and society. ANN NEUROL 2025.},
}

@article {pmid40535306,
year = {2025},
author = {Xavier Fidêncio, A and Grün, F and Klaes, C and Iossifidis, I},
title = {Hybrid brain-computer interface using error-related potential and reinforcement learning.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1569411},
pmid = {40535306},
issn = {1662-5161},
abstract = {Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.},
}

@article {pmid40535187,
year = {2025},
author = {Bao, Y and Zhou, H and Geng, F and Hu, Y},
title = {The relation between game disorder and interruption during game is mediated by game craving.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1579016},
pmid = {40535187},
issn = {1664-1078},
abstract = {The burgeoning user base and potential negative effects of excessive involvement in gaming, particularly Internet Gaming Disorder (IGD), demand significant attention. While existing research has explored the susceptibility of individuals with IGD to game-related stimuli, the question of why it is challenging for these individuals to disengage from gaming remains under-explored. Drawing parallels with the concept of interruption, we hypothesize that negative emotions triggered during gaming interruptions would drive individuals' craving for the game and compelling them to continue playing, reinforcing the IGD cycle. In this study, 42 male 'League of Legends' players, aged 19 to 29, experienced controlled interruptions every 3 min during gaming and non-gaming control tasks. Our findings demonstrate that interruptions during gaming elicited significantly higher levels of anger and anxiety compared to the control tasks. Further, we found a positive correlation between the severity of IGD symptoms and the intensity of anger and anxiety induced by gaming interruptions. Additionally, our analysis suggests that craving partially mediates the relationship between anger arousal during gaming interruptions and IGD severity. These findings provide new insights into how emotional responses to gaming interruptions contribute to IGD, offering a novel perspective for future research and potential treatment approaches.},
}

@article {pmid40534746,
year = {2025},
author = {Zheng, K and Guo, L and Liang, W and Liu, P},
title = {Comparison of the effects of transcranial direct current stimulation combined with different rehabilitation interventions on motor function in people suffering from stroke-related symptoms: a systematic review and network meta-analysis.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1586685},
pmid = {40534746},
issn = {1664-2295},
abstract = {BACKGROUND: This study employs network meta-analysis to assess the efficacy of transcranial direct current stimulation (tDCS) combined with different rehabilitation approaches in enhancing motor function in people suffering from stroke-related symptoms (PSSS). The objective is to determine the most effective tDCS-based rehabilitation approach and offer valuable evidence to guide clinical decision-making.

METHODS: This study included randomized controlled trials (RCTs) published before September 23, 2024. We conducted a systematic search across eight databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Biology Medicine (SinoMed), Wanfang, and VIP. Network meta-analysis (NMA) was conducted utilizing R Studio and Stata 15.0 for data analysis.

RESULTS: A total of 74 RCTs were included in this study, encompassing 4,335 PSSS and 11 intervention strategies. The NMA revealed that brain-computer interface therapy (BCIT) in combination with tDCS [surface under the cumulative ranking curve (SUCRA) = 88.34%] was the most effective tDCS-based intervention for improving the Fugl-Meyer Assessment for Upper Extremity score in PSSS. Mirror therapy (MT) in combination with tDCS (SUCRA = 85.96%) was identified as the optimal intervention for enhancing the Action Research Arm Test score in PSSS. MT + tDCS (SUCRA = 84.29%) was the best approach for improving the Fugl-Meyer Assessment for Lower Extremity score. Additionally, acupuncture and moxibustion (AM) in combination with tDCS (SUCRA = 77.16%) was the most effective intervention for increasing the Berg Balance Scale score in PSSS. The two-dimensional clustering analysis showed that MT + tDCS (SUCRA = 75.83%/85.96%) was the optimal tDCS-based rehabilitation strategy for treating upper limb motor dysfunction in PSSS, while AM+tDCS (SUCRA = 76.94%/77.16%) was the best tDCS-based rehabilitation strategy for improving lower limb motor dysfunction in PSSS.

CONCLUSION: BCIT+tDCS was identified as the optimal tDCS-based rehabilitation strategy for improving upper limb motor ability in PSSS, MT + tDCS was the most effective intervention for enhancing arm mobility, MT + tDCS was the best protocol for improving lower limb motor ability, while AM+tDCS was the best strategy for improving balance ability. Furthermore, MT + tDCS was the optimal tDCS-based rehabilitation approach for treating upper limb motor dysfunction, whereas AM+tDCS was the most effective strategy for addressing lower limb motor dysfunction in PSSS. Future studies may focus on investigating the therapeutic effects of MT combined with tDCS on Berg Balance Scale score in PSSS, as well as the effects of AM combined with tDCS on Action Research Arm Test score, in order to further explore the therapeutic potential of these two intervention strategies.

https://www.crd.york.ac.uk/PROSPERO/view/CRD42024621998, Identifier PROSPERO CRD42024621998.},
}

@article {pmid40534671,
year = {2025},
author = {Zhai, H and Wang, H and Li, H and Wang, X},
title = {The Intersection of Psychedelics and Sleep: Exploring the Impacts on Sleep Architecture, Dream States, and Therapeutic Implications.},
journal = {ACS pharmacology & translational science},
volume = {8},
number = {6},
pages = {1832-1836},
pmid = {40534671},
issn = {2575-9108},
abstract = {The interplay between psychedelics, such as psilocybin, lysergic acid diethylamide (LSD) and dimethyltryptamine (DMT), and sleep is an emerging area, but their impact on sleep remains relatively underexplored. This viewpoint provides a perspective on how psychedelics may alter sleep phases, dreaming, and their potential therapeutic applications for sleep disorders.},
}

@article {pmid40533772,
year = {2025},
author = {Lv, S and Ran, X and Xia, M and Zhang, Y and Pang, T and Zhou, X and Zhao, Z and Yu, Y and Gao, Z},
title = {Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {137},
pmid = {40533772},
issn = {1743-0003},
support = {221100310500//the Major Science and Technology Projects of Henan Province/ ; 82302298//the National Natural Science Foundation of China/ ; 82201709//the National Natural Science Foundation of China/ ; 24IRTSTHN042//Innovative Research Team (in Science and Technology) in University of Henan Province/ ; XTkf01//the Open Project Program of Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder/ ; 242102521012//International Science and Technology Cooperation Project of Henan Province/ ; 242102310055//the Science and Technology Research Project of Henan Province/ ; },
mesh = {Humans ; Male ; Female ; *Electroencephalography/methods ; Middle Aged ; *Brain-Computer Interfaces ; *Imagination/physiology ; *Stroke/physiopathology ; Aged ; *Hand/physiopathology ; *Stroke Rehabilitation/methods ; *Functional Laterality/physiology ; Support Vector Machine ; Adult ; },
abstract = {BACKGROUND: Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. However, the neural dynamics underlying these differences, especially from the perspective of Electroencephalography(EEG) microstate, remain poorly understood in acute stroke patients.

METHODS: This study enrolled 14 acute stroke patients and recorded their EEG data during left and right-hand MI tasks. Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms.

RESULTS: Microstate analysis revealed significant differences in temporal features of microstate A and C during left and right-hand MI tasks. During left-hand MI, microstate A exhibited longer Duration(Pfdr=0.032), higher Occurrence(Pfdr=0.018), and greater Coverage(Pfdr=0.004) compared to the right-hand, whereas microstate C showed the opposite pattern(Pfdr=0.044, Pfdr=0.004, Pfdr=0.004). Additionally, the TP from microstate B→A, D→A and D→C also demonstrated significant differences(Pfdr=0.04, Pfdr<0.001, Pfdr=0.006). Among classification models, the KNN algorithm achieved the highest accuracy of 75.00%, outperforming LDA and SVM. Fisher analysis indicated that the Occurrence of microstate C was the most discriminative feature for distinguishing between left and right-hand MI tasks in acute stroke patients.

CONCLUSION: Differences in EEG microstate features during left and right-hand MI tasks in acute stroke patients may reflect lateralized mechanisms of brain network reorganization. Microstate features hold significant potential for both post-stroke brain function assessment and the optimization of BCI systems. These features could enhance adaptive BCI strategies in acute stroke rehabilitation.},
}

Humans
Male
Female
*Electroencephalography/methods
Middle Aged
*Brain-Computer Interfaces
*Imagination/physiology
*Stroke/physiopathology
Aged
*Hand/physiopathology
*Stroke Rehabilitation/methods
*Functional Laterality/physiology
Support Vector Machine
Adult

@article {pmid40532880,
year = {2025},
author = {Luo, X and Dong, J and Li, T},
title = {Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {119667},
doi = {10.1016/j.jad.2025.119667},
pmid = {40532880},
issn = {1573-2517},
abstract = {BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), major depressive disorder (MDD), and healthy controls, explored the link between cytokine levels and cognitive function, and created machine learning models to evaluate the diagnostic potential of cytokine and cognitive assessments.

METHODS: This study involved 64 early-onset SCZ patients, 53 adolescents with MDD, and 33 healthy controls. The plasma concentrations of 44 cytokines were measured using the LUMINEX multiplex assay. Cognitive function was tested with the Cambridge Neuropsychological Test Automated Battery. Random Forest and Extreme Gradient Boosting models were used for classification, with their effectiveness evaluated via ROC curve analysis.

RESULTS: SCZ patients exhibited significantly elevated levels of CCL11, IL-2 and IL-13, while MDD patients displayed increased CXCL2 and G-CSF levels but decreased CCL20 and CCL11 levels. SCZ patients showed significant cognitive impairments compared to healthy controls. Elevated CCL11 were associated with poorer memory accuracy, and higher G-CSF were linked to worse executive function. The XGBoost model was more sensitive in classifying MDD than the Random Forest model, but both struggled to differentiate SCZ patients from healthy controls due to low specificity.

CONCLUSION: Early-onset SCZ and adolescent MDD patients showed unique peripheral cytokine profiles, with SCZ patients experiencing significant cognitive deficits. The cytokine CCL11 was found to have a significant association with cognitive dysfunction. Cytokine levels and cognitive assessments may serve as potential biomarkers for the diagnosis of MDD.},
}

@article {pmid40530005,
year = {2025},
author = {Shao, W and Meng, W and Zuo, J and Li, X and Ming, D},
title = {Opportunities and Challenges of Brain-on-a-Chip Interfaces.},
journal = {Cyborg and bionic systems (Washington, D.C.)},
volume = {6},
number = {},
pages = {0287},
pmid = {40530005},
issn = {2692-7632},
abstract = {The convergence of life sciences and information technology is driving a new wave of scientific and technological innovation, with brain-on-a-chip interfaces (BoCIs) emerging as a prominent area of focus in the brain-computer interface field. BoCIs aim to create an interactive bridge between lab-grown brains and the external environment, utilizing advanced encoding and decoding technologies alongside electrodes. Unlike classical brain-computer interfaces that rely on human or animal brains, BoCIs employ lab-grown brains, offering greater experimental controllability and scalability. Central to this innovation is the advancement of stem cell and microelectrode array technologies, which facilitate the development of neuro-electrode hybrid structures to ensure effective signal transmission in lab-grown brains. Furthermore, the evolution of BoCI systems depends on a range of stimulation strategies and novel decoding algorithms, including artificial-intelligence-driven methods, which has expanded BoCI applications to pattern recognition and robotic control. Biological neural networks inherently grant BoCI systems neuro-inspired computational properties-such as ultralow energy consumption and dynamic plasticity-that surpass those of conventional artificial intelligence. Functionally, BoCIs offer a novel framework for hybrid intelligence, merging the cognitive capabilities of biological systems (e.g., learning and memory) with the computational efficiency of machines. However, critical challenges span 4 domains: optimizing neural maturation and functional regionalization, engineering high-fidelity bioelectronic interfaces for robust signal transduction, enhancing adaptive neuroplasticity mechanisms in lab-grown brains, and achieving biophysically coherent integration with artificial intelligence architectures. Addressing these limitations could offer insights into emergent intelligence while enabling next-generation biocomputing solutions.},
}

@article {pmid40529543,
year = {2025},
author = {Faber, J and Tsytsarev, V and Pais-Vieira, M and Aksenova, T},
title = {Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies, volume II.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1619232},
pmid = {40529543},
issn = {1662-5161},
}

@article {pmid40527877,
year = {2025},
author = {Moreira, JPC and Carvalho, VR and Mendes, EMAM and Fallah, A and Sejnowski, TJ and Lainscsek, C and Comstock, L},
title = {An open-access EEG dataset for speech decoding: Exploring the role of articulation and coarticulation.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1017},
pmid = {40527877},
issn = {2052-4463},
mesh = {Humans ; *Electroencephalography ; *Speech ; *Brain-Computer Interfaces ; Phonetics ; Transcranial Magnetic Stimulation ; Adult ; Male ; Female ; },
abstract = {Electroencephalography (EEG) holds promise for brain-computer interface (BCI) devices as a non-invasive measure of neural activity. With increased attention to EEG-based BCI systems, publicly available datasets incorporating the complex stimuli found in naturalistic speech are necessary to establish a common standard of performance within the BCI community. Effective solutions must overcome noise in the EEG signal and remain reliable across sessions and stimuli that reflect types of real-world linguistic complexity without overfitting to a dataset or task. We present two validated datasets (N=8 and N=16) for classification at the phoneme and word level and by the articulatory properties of phonemes. EEG signals were recorded from 64 channels while subjects listened to and repeated six consonants and five vowels. Individual phonemes were combined in different phonetic environments to produce coarticulated variation in 40 consonant-vowel pairs, 20 real words, and 20 pseudowords. Phoneme pairs and words were presented during a control condition and during transcranial magnetic stimulation (TMS) to assess whether stimulation would augment the EEG signal associated with specific articulatory processes.},
}

Humans
*Electroencephalography
*Speech
*Brain-Computer Interfaces
Phonetics
Transcranial Magnetic Stimulation
Adult
Male
Female

@article {pmid40527666,
year = {2025},
author = {Toner, AA and Eberlin, L and Pichaimuthu, R and Tompkins, T and Szekeres, M},
title = {The use of robotics and artificial intelligence in upper extremity rehabilitation following traumatic injury: A scoping review.},
journal = {Journal of hand therapy : official journal of the American Society of Hand Therapists},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jht.2025.04.009},
pmid = {40527666},
issn = {1545-004X},
abstract = {BACKGROUND: With the recent advances in technology and its increased use in society, healthcare practices work to identify areas where technology can be implemented to enhance patient care. Rehabilitation has begun to incorporate the use of robotics and artificial intelligence to facilitate positive outcomes and assist in achieving patient goals following injury. While traumatic upper extremity injuries can result in increased levels of pain and disability for an individual, it is not clear how robotics and artificial intelligence have been used in hand rehabilitation to address these issues.

PURPOSE: The objective of this study is to understand the extent of the use of robotics and artificial intelligence for traumatic upper extremity injuries.

STUDY DESIGN: Scoping review.

METHODS: The search strategy was conducted in Embase, CINAHL, MEDLINE, and PsycINFO and identified 7105 studies published between 2014 and 2024. Following title and abstract screening and removal of duplicates, 122 full-text articles were screened. A total of 13 papers were included that used artificial intelligence, robotics, or other technology in rehabilitation programs for individuals with traumatic upper extremity injuries.

RESULTS: Of the 13 included studies: 11 used robotics such as the KINARM Exoskeleton, the Hybrid Assistive Limb, and the WRISTBOT, and two used artificial intelligence including chatbots and brain-computer interface. Multiple outcomes were reported with the most common including range of motion, strength, pain, function, and joint sense.

CONCLUSIONS: Currently, there is a wide variety of different forms of robotics with very little reported use of artificial intelligence for traumatic upper extremity injuries. There exists opportunities for future research to further investigate how these technologies can influence clinical outcomes for patients with traumatic upper extremity injuries.},
}

@article {pmid40527337,
year = {2025},
author = {Temmar, H and Willsey, MS and Costello, JT and Mender, MJ and Cubillos, LH and DeMatteo, JC and Lam, JLW and Wallace, DM and Kelberman, MM and Patil, PG and Chestek, CA},
title = {Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade568},
pmid = {40527337},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. Approach. One adult male rhesus macque was implanted with Utah arrays in the motor cortex and performed a 2D dexterous finger movement task for a juice reward. Multiple linear and nonlinear 'decoders' were used to map from recorded spiking band power into movement kinematics. Performance of these decoders was compared and analyzed to determine how nonlinear decoders perform in both open and closed-loop scenarios. Main Results. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization.

SIGNIFICANCE: The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks. .},
}

@article {pmid40527331,
year = {2025},
author = {Xin, H and Li, H and Qi, H},
title = {A novel paradigm for two-degree-of-freedom BCI control based on ERP in-duced by overt and covert visual attention.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade56a},
pmid = {40527331},
issn = {1741-2552},
abstract = {This study developed a novel brain-computer interface (BCI) paradigm based on event-related potentials (ERPs) to achieve simultaneous two-degree-of-freedom (2-DOF) control through overt and covert visual selective attention. Methods: In this paradigm, three stimuli were arranged equidistantly around the cursor. Participants selected two stimuli as attention targets based on the relative position of the cursor and the intended movement destination, focusing overtly on one while covertly attending to the other. EEG data collected during offline experiments were used to train classifiers for overt and covert targets, and the outputs of these classifiers were employed in online experiments to construct movement vectors for controlling the cursor in a 2D space. Results: EEG analysis demonstrated that overt and covert targets elicited distinct ERP signals, with classification accuracies of 96.2% and 92.4%, respectively. The accuracy of simultaneously identifying both targets reached 91.0%. In online experiments, the success rate of moving the cursor to the target region was 96%, and 91% of cursor movements were in the desired direction. These results confirm the feasibility of achieving 2D control through ERP-based selective attention and validate the effectiveness of the proposed paradigm. Conclusion: This study introduces a novel EEG-based approach for multi-degree-of-freedom control, expanding the capabilities of traditional ERP-based BCIs, which have primarily been limited to single-degree-of-freedom applications. .},
}

@article {pmid40526548,
year = {2025},
author = {Li, C and Cao, Z and Pan, Y and Zhu, P and Li, P and Li, F and Chen, H and Lu, BL and Wan, F and Yao, D and Xu, P},
title = {EEG-Based Emotion Monitoring and Regulation System by Learning the Discriminative Brain Network Manifold.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3576182},
pmid = {40526548},
issn = {2162-2388},
abstract = {Emotion recognition based on electroencephalogram (EEG) is fundamentally associated with human-like intelligence system. However, due to the noise-sensitive characteristics of EEGs and the individual variability of emotions, it is very challenging to extract inherent emotion dependent patterns from emotional EEG signals. In this work, we propose a L1-norm space defined discriminative brain network manifold learning model (L1-SGL), in which the EEG noise outliers can be effectively separated and the pseudolabeled samples caused by subjective feelings can be automatically corrected. Off-line experimental results consistently indicate that the L1-SGL can effectively suppress the influence of noise and achieve an incomparable superiority performance over other existing methods in EEG emotion recognition. Besides, benefiting from the time efficiency of the L1-SGL, an online emotion monitoring and regulation system is further implemented in this work. On-line emotion decoding experimental results (86.30%) of 25 participants prove that the L1-SGL can effectively satisfy the real-time requirements of on-line emotional monitoring applications, and the significant negative emotion regulation experimental results ($p \lt 0.001$) further confirm the feasibility and effectiveness of L1-SGL model in real-time emotion regulation and interactive applications. Overall, the L1-SGL provides a promising solution for the real-time online affective brain-computer interfaces (aBCIs) and the intelligent clinical closed-loop treatments.},
}

@article {pmid40526539,
year = {2025},
author = {Jia, T and Long, H and Ji, L and Guan, X},
title = {EEG-based Spatial-Channel Interaction Attention Neural Networks for Detecting Empathy in Motor Collaboration.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580617},
pmid = {40526539},
issn = {2168-2208},
abstract = {Embodied intelligence and humanoid robots aim to mimic interpersonal interactions to achieve affective human-robot interaction (HRI). A major challenge in advancing HRI lies in effectively emulating interpersonal affective interactions and evaluating the resulting artificial empathy. To address these challenges, we propose SpatialChannel Interaction Attention Neural Networks (SCIANN)-a novel EEG-based architecture that combines topological brain activation and connectivity patterns to decode empathy in motor collaboration. A private EEG dataset from a collaborative brain-computer interface motor control experiment and a public EEG dataset from a dyadic perceptual crossing experiment were used for evaluating SCIANN's performance with comparisons with five baseline models. Results showed that SCIANN outperformed the state-of-the-art baseline models. In the private dataset, SCIANN reached an accuracy of 100% both in inter-subject and cross-subject tests for detecting whether empathy is induced or not. For classifying 4-class empathy, it achieved an accuracy of 98.3% in the inter-subject test, and 48.1% in the cross-subject test. In the public dataset, SCIANN reached a classification accuracy of 92.2% in inter-subject and 91.7% in cross-subject tests for detecting whether empathy is induced or not. Feature visualization results revealed that contributing EEG channel importance features and channel interaction features aligned with established neurophysiological findings. These results collectively demonstrate SCIANN's potential as a robust, generalizable framework for artificial empathy assessment in HRI applications.},
}

@article {pmid40526534,
year = {2025},
author = {Luo, T and Zhang, J and Qiu, Y and Zhang, L and Hu, Y and Yu, Z and Liang, Z},
title = {M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580612},
pmid = {40526534},
issn = {2168-2208},
abstract = {Emotion decoding using Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs) is crucial for affective computing but is hindered by EEG's non-stationarity, individual variability, and the high cost of large-scale labeled data. Deep learning-based approaches, while effective, require substantial computational resources and large datasets, limiting their practicality. To address these challenges, we propose Manifold-based Domain Adaptation with Dynamic Distribution (M3D), a lightweight non-deep transfer learning framework. M3D includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning. The data undergoes a transformation onto an optimal Grassmann manifold space, enabling dynamic alignment of the source and target domains. This process prioritizes both marginal and conditional distributions according to their significance, ensuring enhanced adaptation efficiency across various types of data. In the classifier learning, the principle of structural risk minimization is integrated to develop robust classification models. This is complemented by dynamic distribution alignment, which refines the classifier iteratively. Additionally, the ensemble learning module aggregates the classifiers obtained at different stages of the optimization process, which leverages the diversity of the classifiers to enhance the overall prediction accuracy. The proposed M3D framework is evaluated on three benchmark EEG emotion recognition datasets using two validation protocols (cross-subject single-session and cross-subject cross-session), as well as on a clinical EEG dataset of Major Depressive Disorder (MDD). Experimental results demonstrate that M3D outperforms traditional non-deep learning methods, achieving an average improvement of 6.67%, while achieving deep learning-comparable performance with significantly lower data and computational requirements. These findings highlight the potential of M3D to enhance the practicality and applicability of aBCIs in real-world scenarios.},
}

@article {pmid40524963,
year = {2025},
author = {Zhao, W and Lu, H and Zhang, B and Zheng, X and Wang, W and Zhou, H},
title = {TCANet: a temporal convolutional attention network for motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {91},
pmid = {40524963},
issn = {1871-4080},
abstract = {Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and variability of MI-EEG signals. This study proposes the Temporal Convolutional Attention Network (TCANet), a novel end-to-end model that hierarchically captures spatiotemporal dependencies by progressively integrating local, fused, and global features. Specifically, TCANet employs a multi-scale convolutional module to extract local spatiotemporal representations across multiple temporal resolutions. A temporal convolutional module then fuses and compresses these multi-scale features while modeling both short- and long-term dependencies. Subsequently, a stacked multi-head self-attention mechanism refines the global representations, followed by a fully connected layer that performs MI-EEG classification. The proposed model was systematically evaluated on the BCI IV-2a and IV-2b datasets under both subject-dependent and subject-independent settings. In subject-dependent classification, TCANet achieved accuracies of 83.06% and 88.52% on BCI IV-2a and IV-2b respectively, with corresponding Kappa values of 0.7742 and 0.7703, outperforming multiple representative baselines. In the more challenging subject-independent setting, TCANet achieved competitive performance on IV-2a and demonstrated potential for improvement on IV-2b. The code is available at https://github.com/snailpt/TCANet.},
}

@article {pmid40523119,
year = {2025},
author = {Kamitani, Y and Tanaka, M and Shirakawa, K},
title = {Visual Image Reconstruction from Brain Activity via Latent Representation.},
journal = {Annual review of vision science},
volume = {},
number = {},
pages = {},
doi = {10.1146/annurev-vision-110423-023616},
pmid = {40523119},
issn = {2374-4650},
abstract = {Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and generative models. This review traces the field's evolution from early classification approaches to sophisticated reconstructions that capture detailed, subjective visual experiences, emphasizing the roles of hierarchical latent representations, compositional strategies, and modular architectures. Despite notable progress, challenges remain, such as achieving true zero-shot generalization for unseen images and accurately modeling the complex, subjective aspects of perception. We discuss the need for diverse datasets, refined evaluation metrics aligned with human perceptual judgments, and compositional representations that strengthen model robustness and generalizability. Ethical issues, including privacy, consent, and potential misuse, are underscored as critical considerations for responsible development. Visual image reconstruction offers promising insights into neural coding and enables new psychological measurements of visual experiences, with applications spanning clinical diagnostics and brain-machine interfaces.},
}

@article {pmid40522886,
year = {2025},
author = {Nam, J and Shin, H and You, C and Baeg, E and Kim, JG and Yang, S and Han, MR},
title = {Cortical Stimulation-Based Transcriptome Shifts on Parkinson's Disease Animal Model.},
journal = {ASN neuro},
volume = {17},
number = {1},
pages = {2513881},
doi = {10.1080/17590914.2025.2513881},
pmid = {40522886},
issn = {1759-0914},
mesh = {Animals ; *Transcriptome/physiology ; Male ; Disease Models, Animal ; *Motor Cortex/metabolism ; *Parkinson Disease/genetics/therapy/metabolism ; Mice ; Mice, Inbred C57BL ; },
abstract = {Parkinson's disease is the second most prevalent neurodegenerative disorder and is characterized by the degeneration of dopaminergic neurons. Significant improvements in gait balance, particularly in step length and velocity, were observed with less invasive wireless cortical stimulation. Transcriptome sequencing was performed to demonstrate the cellular mechanism, specifically targeting the primary motor cortex, where stimulation was applied. Our findings indicated that 38 differentially expressed genes (DEGs), initially downregulated following Parkinson's disease induction, were subsequently restored to normal levels after cortical stimulation. These 38 DEGs are potential targets for the treatment of motor disorders in Parkinson's disease. These genes are implicated in crucial processes, such as astrocyte-mediated blood vessel development and microglia-mediated phagocytosis of damaged motor neurons, suggesting their significant roles in improving behavioral disorders. Moreover, these biomarkers not only facilitate the rapid and accurate diagnosis of Parkinson's disease but also assist in precision medicine approaches.},
}

Animals
*Transcriptome/physiology
Male
Disease Models, Animal
*Motor Cortex/metabolism
*Parkinson Disease/genetics/therapy/metabolism
Mice
Mice, Inbred C57BL

@article {pmid40522806,
year = {2025},
author = {Wang, P and Qi, Y and Pan, G},
title = {Partial Domain Adaptation for Stable Neural Decoding in Disentangled Latent Subspaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3577222},
pmid = {40522806},
issn = {1558-2531},
abstract = {OBJECTIVE: Brain-Computer Interfaces (BCI) have demonstrated significant potential in neural rehabilitation. However, the variability of non-stationary neural signals often leads to instabilities of behavioral decoding, posing critical obstacles to chronic applications. Domain adaptation technique offers a promising solution by obtaining the invariant neural representation against non-stationary signals through distribution alignment. Here, we demonstrate domain adaptation that directly applied to neural data may lead to unstable performance, mostly due to the common presence of task-irrelevant components within neural signals. To address this, we aim to identify task-relevant components to achieve more stable neural alignment.

METHODS: In this work, we propose a novel partial domain adaptation (PDA) framework that performs neural alignment within the task-relevant latent subspace. With pre-aligned short-time windows as input, the proposed latent space is constructed based on a causal dynamical system, enabling more flexible neural decoding. Within this latent space, task-relevant dynamical features are disentangled from task-irrelevant components through VAE-based representation learning and adversarial alignment. The aligned task-relevant features are then employed for neural decoding across domains.

RESULTS: Using Lyapunov theory, we analytically validated the improved stability of late our neural representations through alignment. Experiments with various neural datasets verified that PDA significantly enhanced the cross-session decoding performance.

CONCLUSION: PDA successfully achieved stable neural representations across different experimental days, enabling reliable long-term decoding.

SIGNIFICANCE: Our approach provides a novel aspect for addressing the challenge of chronic reliability in real-world BCI deployments.},
}

@article {pmid40522801,
year = {2025},
author = {Yao, Y and Swaef, W and Geirnaert, S and Bertrand, A},
title = {EEG-Based Decoding of Selective Visual Attention in Superimposed Videos.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3580261},
pmid = {40522801},
issn = {2168-2208},
abstract = {Selective attention enables humans to efficiently process visual stimuli by enhancing important elements and filtering out irrelevant information. Locating visual attention is fundamental in neuroscience with potential applications in brain-computer interfaces. Conventional paradigms often use synthetic stimuli or static images, but visual stimuli in real life contain smooth and highly irregular dynamics. We show that these irregular dynamics can be decoded from electroencephalography (EEG) signals for selective visual attention decoding. To this end, we propose a free-viewing paradigm in which participants attend to one of two superimposed videos, each showing a center-aligned person performing a stage act. Superimposing ensures that the relative differences in the neural responses are not driven by differences in object locations. A stimulus-informed decoder is trained to extract EEG components correlated with the motion patterns of the attended object, and can detect the attended object in unseen data with significantly above-chance accuracy. This shows that the EEG responses to naturalistic motion are modulated by selective attention. Eye movements are also found to be correlated to the motion patterns in the attended video, despite the spatial overlap with the distractor. We further show that these eye movements do not dominantly drive the EEG-based decoding and that complementary information exists in EEG and gaze data. Moreover, our results indicate that EEG may also capture neural responses to unattended objects. To our knowledge, this study is the first to explore EEG-based selective visual attention decoding on natural videos, opening new possibilities for experiment design.},
}

@article {pmid40522765,
year = {2026},
author = {Kinfe, T and Brenner, S and Etminan, N},
title = {Brain-computer interfaces re-shape functional neurosurgery.},
journal = {Neural regeneration research},
volume = {21},
number = {3},
pages = {1122-1123},
doi = {10.4103/NRR.NRR-D-24-01336},
pmid = {40522765},
issn = {1673-5374},
}

@article {pmid40522539,
year = {2025},
author = {Ullah, A and Bookwalter, J and Sant, H and Azapagic, A and Shea, J and Berlet, R and Jha, N and Bailes, J and Gale, BK},
title = {An Osmosis-driven 3D-printed brain implant for drug delivery.},
journal = {Biomedical microdevices},
volume = {27},
number = {3},
pages = {29},
pmid = {40522539},
issn = {1572-8781},
mesh = {*Printing, Three-Dimensional ; *Osmosis ; *Drug Delivery Systems/instrumentation ; *Brain/surgery/metabolism ; *Brain Neoplasms/drug therapy ; Humans ; *Prostheses and Implants ; Glioblastoma/drug therapy ; },
abstract = {Glioblastoma is a highly malignant brain tumor with limited survival rates due to challenges in complete surgical excision, high recurrence (> 90%), and the inefficacy of systemic drug delivery. Significant efforts have been made to develop drug-loaded brain implants, catheters, and wafers aimed at enhancing survival rates by suppressing tumor recurrence. However, these devices often fail due to clogging, reflux, and the inability to be fully implanted intracranially. Furthermore, a lack of tissue penetration, diffusion distance, and duration of therapy have limited effectiveness of these implants. To address existing challenges, this study reports an osmosis-driven, 3D-printed brain implant with the potential for precise device customization to meet therapeutic needs, while negating systemic toxicity. It is capable of being loaded with two distinct therapeutic agents and implanted directly into the tumor resection cavity during surgery. The device features dual reservoirs, osmotic membranes, and precision-engineered needles for anchoring the device in the resection cavity and perfusing. Further, the device was characterized in vitro using 0.2% agarose gel as a brain tissue analog, with food dye as a drug analog and sodium chloride serving as an osmogen. A design of experiment approach was implemented to investigate various parameters, including membrane pore size, osmogen concentration, needle length, and their effects on release rates. The results demonstrated that the optimized implant achieves flow rates of 2.5 ± 0.1 µl/Hr and diffusion distance of up to 15.5 ± 0.4 mm, using 25 nm pore osmotic membranes with 25.3% osmogen concentration, aligning with model predictions.},
}

*Printing, Three-Dimensional
*Osmosis
*Drug Delivery Systems/instrumentation
*Brain/surgery/metabolism
*Brain Neoplasms/drug therapy
Humans
*Prostheses and Implants
Glioblastoma/drug therapy

@article {pmid40520823,
year = {2025},
author = {Ivan Brown, A and MacDuffie, KE and Goering, S and Klein, E},
title = {The "wheels that keep me goin'": invisible forms of support for brain pioneers.},
journal = {Neuroethics},
volume = {18},
number = {1},
pages = {},
pmid = {40520823},
issn = {1874-5490},
abstract = {Research participants in long-term, first-in-human trials of implantable neural devices (i.e., brain pioneers) are critical to the success of the emerging field of neurotechnology. How these participants fare in studies can make or break a research program. Yet, their ability to enroll, participate, and seamlessly exit studies relies on both the support of family/caregivers and care from researchers that is often hidden from view. The present study offers an initial exploration of the different kinds of support that play a role in neural device trials from the perspectives of brain pioneers and their support partners (spouses, paid caregivers, parents, etc.). Using a mixed methods approach (semi-structured, open-ended interviews and a survey) with interpretive grounded theory, we present narratives from a study of six pioneers -- four in brain-computer interface (BCI) trials, and two in deep brain stimulation (DBS) trials -- and five support partners, about their experiences of being supported and supporting participants in implantable neural device studies. Our findings indicate the substantial amount of work involved on the part of pioneers - and some support partners - to make these studies successful. A central finding of the study is that non-logistical forms of support - social, emotional, and epistemic support - play a role, alongside more widely acknowledged forms of support, such as transportation and physical and clinical care. We argue that developing a better understanding of the kinds of support that enable neurotechnology studies to go well can help bridge the gap between abstract ethical principles of caring for subjects and on-the-ground practice.},
}

@article {pmid40520820,
year = {2025},
author = {Aubinet, C and Gillet, A and Regnier, A},
title = {Disorders of Consciousness, Language and Communication Following Severe Brain Injury.},
journal = {Psychologica Belgica},
volume = {65},
number = {1},
pages = {169-188},
pmid = {40520820},
issn = {2054-670X},
abstract = {Patients with severe brain injuries and disorders of consciousness (DoC) represent a complex clinical population in terms of diagnosis, prognosis, and management, including critical ethical considerations. Behavioral assessment scales remain the primary tools for evaluating the level of consciousness of these patients following a coma; however, they heavily depend on language and communication abilities. This reliance can lead to underestimating residual consciousness in cases where language impairments go undetected. Accordingly, the latest international guidelines on DoC diagnosis have highlighted aphasia as a significant confounding factor that must be addressed. On the other hand, accurately assessing residual language abilities is essential for better characterizing the patient's cognitive profile. This, in turn, enables neuropsychologists and speech-language therapists to tailor and plan effective rehabilitation programs. This review examines the current literature on language function and communication skills in patients with DoC, detailing the latest tools for assessing and managing language and consciousness in individuals with severe brain injuries. We explore the critical role of language function in evaluating residual consciousness, particularly in DoC behavioral diagnoses and in identifying covert consciousness through neuroimaging passive or active paradigms. Furthermore, we discuss how therapies aimed at recovering consciousness-such as pharmacological treatments, electromagnetic therapies, sensory or cognitive stimulation, and communication aids like brain-computer interfaces-may also impact or rely on language function and communication abilities. Further research is needed to refine methodologies and better understand the interplay between language processing, communication and levels of consciousness.},
}

@article {pmid40519866,
year = {2025},
author = {Wang, K and Ren, S and Jia, Y and Yan, X and Wang, L and Fan, Y},
title = {Neuromorphic chips for biomedical engineering.},
journal = {Mechanobiology in medicine},
volume = {3},
number = {3},
pages = {100133},
pmid = {40519866},
issn = {2949-9070},
abstract = {The modern medical field faces two critical challenges: the dramatic increase in data complexity and the explosive growth in data size. Especially in current research, medical diagnostic, and data processing devices relying on traditional computer architecture are increasingly showing limitations when faced with dynamic temporal and spatial processing requirements, as well as high-dimensional data processing tasks. Neuromorphic devices provide a new way for biomedical data processing due to their low energy consumption and high dynamic information processing capabilities. This paper aims to reveal the advantages of neuromorphic devices in biomedical applications. First, this review emphasizes the urgent need of biomedical engineering for diversify clinical diagnostic techniques. Secondly, the feasibility of the application in biomedical engineering is demonstrated by reviewing the historical development of neuromorphic devices from basic modeling to multimodal signal processing. In addition, this paper demonstrates the great potential of neuromorphic chips for application in the fields of biosensing technology, medical image processing and generation, rehabilitation medical engineering, and brain-computer interfaces. Finally, this review provides the pathways for constructing standardized experimental protocols using biocompatible technologies, personalized treatment strategies, and systematic clinical validation. In summary, neuromorphic devices will drive technological innovation in the biomedical field and make significant contributions to life health.},
}

@article {pmid40519178,
year = {2025},
author = {Kumar, R and Waisberg, E and Ong, J and Lee, AG},
title = {Response to letter to the editor on "the potential power of neuralink - how brain-machine interfaces can revolutionize medicine".},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2521399},
pmid = {40519178},
issn = {1745-2422},
}

@article {pmid40519177,
year = {2025},
author = {Cordero, DA},
title = {Letter to the editor on "the potential power of neuralink - how brain-machine interfaces can revolutionize medicine".},
journal = {Expert review of medical devices},
volume = {},
number = {},
pages = {},
doi = {10.1080/17434440.2025.2521393},
pmid = {40519177},
issn = {1745-2422},
}

@article {pmid40514107,
year = {2025},
author = {Zuo, M and Chen, X and Sui, L},
title = {A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.},
journal = {Medical engineering & physics},
volume = {141},
number = {},
pages = {104363},
doi = {10.1016/j.medengphy.2025.104363},
pmid = {40514107},
issn = {1873-4030},
mesh = {*Virtual Reality ; *Electroencephalography ; Humans ; Male ; *Signal Processing, Computer-Assisted ; Adult ; Brain-Computer Interfaces ; Female ; Young Adult ; Attention ; *Neural Networks, Computer ; },
abstract = {Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, education, and healthcare, offering more immersive and interactive experiences than traditional 2D formats. This study investigates EEG classification in response to 2D and 3D VR stimuli to deepen our understanding of the neural mechanisms driving VR interactions, with implications for brain-computer interfaces (BCIs). We introduce STA-EEGNet, an innovative model that enhances EEGNet by incorporating spatial-temporal attention (STA), improving EEG signal classification from VR environments. A one-way analysis of variance (ANOVA) was utilized to optimize channel selection, enhancing model accuracy. Comparative experiments showed that STA-EEGNet surpassed traditional EEGNet, achieving a peak accuracy of 99.78 % with channel selection. These findings highlight the benefits of spatial-temporal attention and optimal channel selection in classifying VR-evoked EEG data. This study underscores the importance of integrating spatial-temporal attention with compact convolutional neural networks like EEGNet, not only improving EEG signal classification but also advancing neural decoding and optimizing BCI applications.},
}

*Virtual Reality
*Electroencephalography
Humans
Male
*Signal Processing, Computer-Assisted
Adult
Brain-Computer Interfaces
Female
Young Adult
Attention
*Neural Networks, Computer

@article {pmid40513959,
year = {2025},
author = {Yang, Q and Guo, W and Wang, L and Zhang, Y and Tian, Y and Ming, D and Xiao, X and Yang, J},
title = {Effects of Fstl1 on neuroinflammation and microglia activation in lipopolysaccharide-induced acute depression-like mice.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115696},
doi = {10.1016/j.bbr.2025.115696},
pmid = {40513959},
issn = {1872-7549},
abstract = {Depression is the most prevalent psychiatric illness, and its pathogenesis is associated with neuroinflammation. Follistatinlike protein 1 (FSTL1), a novel inflammatory protein, participates in the pathogenesis of diseases related to neuroinflammation. Therefore, we aimed to investigate the effect of FSTL1 in the pathogenesis of depression mediated using neuroinflammation-mediated models. Our results showed that lipopolysaccharide (LPS) administration could induce despair-like behavior and increase proinflammatory cytokine levels in both male and female mice. Then, a significant positive correlation between hippocampal Fstl1 mRNA expression, microglial activation and desperate-like behaviors was observed in male mice. Moreover, knockdown FSTL1 significantly reduced microglial activation and the expression of proinflammatory cytokines, while overexpression of Fstl1 in hippocampus could exacerbate the activation of microglial under the LPS-induced condition in male mice. Mechanically, knockdown Fstl1 inhibited LPS-induced activation of BV2 microglia and reduced the production of proinflammatory cytokines, thereby protecting the survival of HT22 neurons. In conclusion, our results implied that Fstl1 may modulate despair-like behaviors through regulation of microglial activation and neuronal viability, which would lay the experimental and theoretical foundation for the neuroinflammatory mechanisms underlying depression.},
}

@article {pmid40513226,
year = {2025},
author = {Vasilyev, AN and Svirin, EP and Dubynin, IA and Butorina, AV and Nuzhdin, YO and Ossadtchi, AE and Stroganova, TA and Shishkin, SL},
title = {Intentionally versus spontaneously prolonged Gaze: A MEG study of active gaze-based interaction.},
journal = {Cortex; a journal devoted to the study of the nervous system and behavior},
volume = {189},
number = {},
pages = {76-96},
doi = {10.1016/j.cortex.2025.05.010},
pmid = {40513226},
issn = {1973-8102},
abstract = {Eye fixations are increasingly employed to control computers through gaze-sensitive interfaces, yet the brain mechanisms supporting this non-visual use of gaze remain poorly understood. In this study, we employed 306-channel magnetoencephalography (MEG) to find out what is specific to brain activity when gaze is used voluntarily for control. MEG was recorded while participants played a video game controlled by their eye movements. Each move required object selection by fixating it for at least 500 msec. Gaze dwells were classified as intentional if followed by a confirmation gaze on a designated location and as spontaneous otherwise. We identified both induced oscillatory and sustained phase-locked MEG activity differentiating intentional and spontaneous gaze dwells. Induced power analysis revealed prominent alpha-beta band synchronization (8-30 Hz) localized in the frontal cortex, with location broadly consistent with the frontal eye fields. This synchronization began 500-750 msec before intentional fixation onset and peaked shortly after it, suggesting proactive inhibition of saccadic activity. Sustained evoked responses further distinguished the two conditions, showing gradually rising cortical activation with a maximum at 200 msec post-onset in the inferior temporal cortex during intentional fixations, likely indicative of focused attentional engagement on spatial targets. These findings illuminate the neural dynamics underlying intentional gaze control, shedding light on the roles of proactive inhibitory mechanisms and attentional processes in voluntary behavior. By leveraging a naturalistic gaze-based interaction paradigm, this study offers a novel framework for investigating voluntary control under free behavior conditions and holds potential applications for enhancing hybrid eye-brain-computer interfaces.},
}

@article {pmid40512634,
year = {2025},
author = {Yan, T and Ming, Z and Huang, Y and Liu, Z and Chen, Q and Zhang, D and Liu, M and Suo, D and Zhang, J and Liu, S},
title = {Enhanced Brain-Controlled Mobile Robot based on SE-VEP Paradigm with Single Stimulus.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3579373},
pmid = {40512634},
issn = {1558-0210},
abstract = {Brain-computer interface (BCI) systems based on steady-state visually evoked potentials (SSVEPs) have been widely adopted because of their efficiency and accuracy. However, the traditional SSVEP method has limitations, including visual fatigue and interference between different stimuli. To address these issues, a new BCI paradigm, namely, a spatial encoding-visually evoked potential (SE-VEP) model, is proposed in this work. This paradigm involves deploying four target points to implement gaze restrictions around a stimulus block and optimizing the locations of these target points through offline data acquisition. This design facilitates electroencephalogram (EEG) encoding for four instructions while using only one stimulus block. Data with varying eccentricities are classified using the Riemann kernel-based support vector machine (R-SVM) approach, which achieves a classification accuracy of up to 86.11%. As the eccentricity increases, the classification accuracy initially increases but subsequently decreases. By evaluating the information transfer rate (ITR), the optimal time window length for online BCIs is determined to be 1.2 s. Additionally, an online brain-controlled robotic virtual system is developed to validate the feasibility of the proposed paradigm for online brain-computer interface applications. The results confirm the effectiveness of the proposed paradigm in implementing an online BCI control system. An evaluation conducted with scales and the information transfer rate for a single stimulus (ITRSS) indicates that compared with the traditional BCI system, the proposed paradigm yields greater reductions in user fatigue (2.8 ± 0.5 vs. 4.1 ± 0.6) and stimulus block utilization (24.6 ± 2.3 vs. 8.2 ± 1.1 bits/min).},
}

@article {pmid40511861,
year = {2025},
author = {Ramirez, P},
title = {Alternative ways to access AAC technologies.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-3},
doi = {10.1080/07434618.2025.2513902},
pmid = {40511861},
issn = {1477-3848},
abstract = {More than 21 years ago, I had a car accident that led to a brain stem stroke, leaving me paralyzed and unable to speak. I was desperate to communicate. One day, my sister wrote down the alphabet and pointed to each letter accordingly. I nodded, yes or no, and she wrote my message down. Later, I used a laser light with a letter board and then a laptop with a head pointer. More recently, I started using a gyroscopic air mouse. During outings, I use the laser and the letter board. They are easy to carry and use. Plus, I can communicate in English and Spanish which is very important because my family does not speak English. I am currently enrolled in a clinical trial at the University of California, San Francisco to investigate brain computer interface to control a robotic arm and communicate. They placed an implant in the surface of my brain; the implant connects to a computer system that collects brain signals and translates neural activity from my sensorimotor cortex into intended speech and motor actions. This type of research is needed to enhance communication and improve lives.},
}

@article {pmid40510263,
year = {2025},
author = {Amande, TJ and Kaszyk, V and Brown, F},
title = {Identification of OqxB Efflux Pump and Tigecycline Resistance Gene Cluster tmexC3D2-toprJ3 in Multidrug-Resistant Pseudomonas Stutzeri Isolate G3.},
journal = {Infection and drug resistance},
volume = {18},
number = {},
pages = {2889-2899},
pmid = {40510263},
issn = {1178-6973},
abstract = {PURPOSE: To identify antibiotic resistance genes (ARGs) and understand the molecular basis of multidrug resistance in P. stutzeri isolate G3.

METHODS: Whole-genome sequencing of isolate G3 was conducted at 30X coverage using Illumina NovaSeq 6000. Reads were trimmed using Trimmomatic and assessed using a combination of scripts that incorporated Samtools, BedTools, and bwa-mem. De novo assembly was performed using SPAdes, and assembly metrics were evaluated using QUAST. The assembled genome was uploaded to a Type Strain Genome Server (TYGS) for taxonomic identification. Genome annotation was performed using the KBase and Proksee software using PROKKA. ARGs were identified using the Comprehensive Antibiotic Resistance Database (CARD).

RESULTS: P. stutzeri isolate G3 demonstrated resistance to most antibiotics tested, including meropenem (10 µg), ciprofloxacin (5 µg), gentamicin (10 µg), and tetracycline (30 µg). The ARGs identified were PmpM, AdeF, rsmA, vgb(A), BcI, cipA, OCH-2, and tet(45). A tigecycline-resistant gene cluster, tmexC3D2-toprJ3, was found in NODE_84, while the oqxB gene, encoding a resistance-nodulation-division (RND) efflux pump, was in NODE_309. Phylogenetic analysis showed OqxB clustered with Pseudomonas species, distinct from Klebsiella and Enterobacter. Comparative analysis of oqxB revealed P. stutzeri isolate G3 shared 78-100% identity with Pseudomonas aeruginosa strain 1334/14 in key components of the multidrug efflux system, including the transcriptional regulator MexT, periplasmic adaptor subunit MexE, and permease subunit MexF.

CONCLUSION: Our findings offer new insights into the reservoir of ARGs in the draft genome of Pseudomonas stutzeri isolate G3, including the tmexC3D2-toprJ3 and oqxB genes, highlighting its genomic plasticity and public health significance. This adaptability enables P. stutzeri to thrive in clinical environments, despite its natural habitat association. This study advances our understanding of the molecular mechanisms driving resistance in P. stutzeri and offers valuable insights to inform strategies for combating the spread of antimicrobial resistance in clinical and environmental settings.},
}

@article {pmid40510210,
year = {2025},
author = {Gazerani, P},
title = {A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1587296},
pmid = {40510210},
issn = {1664-2295},
abstract = {OBJECTIVE: The co-occurrence of migraines and depression presents a critical clinical challenge, affecting up to 50% of individuals with either condition. This comorbidity leads to greater disability, higher healthcare costs, and poorer treatment outcomes than either disorder alone. Despite a bidirectional pathophysiological relationship, current models remain static and fragmented, treating each condition separately. This paper proposes a Hybrid Digital-4E Strategy, deployed on an AI-driven neuroadaptive digital health platform, integrating closed-loop therapy, digital phenotyping, and exposome tracking to enable real-time, personalized care.

METHODS: Grounded in the 4E cognition framework (Embodied, Embedded, Enactive, and Extended cognition), this strategy reconceptualizes migraine-depression as an interactive system rather than two separate conditions. The platform integrates real-time biomarker tracking, neuromorphic AI, and precision environmental analytics to dynamically optimize treatment. Adaptive chronotherapy, brain-computer interfaces (BCIs), and virtual reality (VR)-based neuroplasticity training further enhance intervention precision.

CONCLUSION: A closed-loop, AI-driven neuroadaptive system could improve outcomes by enabling early detection, real-time intervention, and precision care tailored to individual neurophysiological and environmental profiles. Addressing AI bias, data privacy, and clinical validation is crucial for implementation. If validated, this Hybrid Digital-4E Strategy could redefine migraine-depression management, paving the way for precision neuropsychiatry.},
}

@article {pmid40506548,
year = {2025},
author = {Wairagkar, M and Card, NS and Singer-Clark, T and Hou, X and Iacobacci, C and Miller, LM and Hochberg, LR and Brandman, DM and Stavisky, SD},
title = {An instantaneous voice-synthesis neuroprosthesis.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {40506548},
issn = {1476-4687},
abstract = {Brain-computer interfaces (BCIs) have the potential to restore communication for people who have lost the ability to speak owing to a neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text[1-3]. However, text communication fails to capture the nuances of human speech, such as prosody and immediately hearing one's own voice. Here we demonstrate a brain-to-voice neuroprosthesis that instantaneously synthesizes voice with closed-loop audio feedback by decoding neural activity from 256 microelectrodes implanted into the ventral precentral gyrus of a man with amyotrophic lateral sclerosis and severe dysarthria. We overcame the challenge of lacking ground-truth speech for training the neural decoder and were able to accurately synthesize his voice. Along with phonemic content, we were also able to decode paralinguistic features from intracortical activity, enabling the participant to modulate his BCI-synthesized voice in real time to change intonation and sing short melodies. These results demonstrate the feasibility of enabling people with paralysis to speak intelligibly and expressively through a BCI.},
}

@article {pmid40506484,
year = {2025},
author = {Liu, J and Liu, H and Zhu, J and Han, X and Bai, Y and Ni, G and Ming, D},
title = {A Dataset of Pinna-Related Transfer Functions Using High-Resolution Pinna Models.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {992},
pmid = {40506484},
issn = {2052-4463},
mesh = {Humans ; *Sound Localization ; *Ear Auricle/physiology ; Acoustics ; },
abstract = {The pinna-related transfer function (PRTF) is critical for localizing and perceiving sound in three-dimensional space. PRTF largely depends on individual spectral cues and the unique physiology of the pinna, necessitating high-resolution data for accurate acoustic modeling. The accuracy of personalized acoustic models could be significantly improved using high-precision physiological data and incorporating advanced simulation methods such as the boundary element method (BEM). We describe a comprehensive dataset of 150 bilateral PRTFs from 75 participants to support developing, improving, and validating personalized PRTF modeling methods. The dataset includes simulated results from binaural laser-scanned models that are accurately validated through empirical measurements. This comprehensive dataset will contribute to acoustic and spatial audio research and support the ongoing advancements in personalized PRTF modeling techniques.},
}

Humans
*Sound Localization
*Ear Auricle/physiology
Acoustics

@article {pmid40505916,
year = {2025},
author = {Bikiaris, RE and Matschek, NI and Koumentakou, I and Niti, A and Kyzas, GZ},
title = {Synergistic effects of arginine and tannic acid on chitosan matrices: An approach for hemostatic sponge development.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {145105},
doi = {10.1016/j.ijbiomac.2025.145105},
pmid = {40505916},
issn = {1879-0003},
abstract = {This study presents the development of a novel multifunctional hydrogel biocomposite sponge designed to address the complexities of wound healing, including rapid hemostasis, infection prevention, and tissue regeneration. Recognizing the limitations of conventional wound dressings that lack multifunctionality, this study introduces a 3D chitosan/tannic acid (CS/TA) hydrogel. After testing three chitosan/tannic acid (CS/TA) ratios, CS/TA-1 (1:0.16), CS/TA-2 (1:0.25), and CS/TA-3 (1:0.34), the most effective formulation, CS/TA-2, was enhanced with sodium alginate (SA) and arginine (Arg) for optimal performance. Arginine, with its guanidinium functional group, served as a green crosslinker through physical interactions, enhancing the sponge's mechanical strength while also improving its hemostatic performance and biocompatibility, promoting cellular interactions. Its inclusion significantly amplified antioxidant activity (>90 %), mitigating oxidative stress and contributing to enhanced therapeutic outcomes. Ionic crosslinking and freeze-drying created a porous, absorbent sponge with high water retention and compression resilience. SEM confirmed the sponge's interconnected porosity, enabling cell infiltration and nutrient exchange. Blood Clotting Index (BCI) assessments demonstrated the hemostatic effectiveness of CS/TA/SA/Arg-3, with 25 % BCI at 5 min and 20 % at 15 min, along with excellent hemocompatibility, achieving a 2.08 % hemolysis rate. These results suggest the hydrogel sponge's potential for effective wound management in emergencies and clinical applications.},
}

@article {pmid40503091,
year = {2025},
author = {Li, Q and Pan, Y},
title = {Mobile eye-tracking and neuroimaging technologies reveal teaching and learning on the move: bibliometric mapping and content analysis.},
journal = {Psychoradiology},
volume = {5},
number = {},
pages = {kkaf013},
pmid = {40503091},
issn = {2634-4416},
abstract = {Mobile psychophysiological technologies, such as portable eye tracking, electroencephalography, and functional near-infrared spectroscopy, are advancing ecologically valid findings in cognitive and educational neuroscience research. Staying informed on the field's current status and main themes requires continuous updates. Here, we conducted a bibliometric and text-based content analysis on 135 articles from Web of Science, specifically parsing publication trends, identifying prolific journals, authors, institutions, and countries, along with influential articles, and visualizing the characteristics of cooperation among authors, institutions, and countries. Using a keyword co-occurrence analysis, five clusters of research trends were identified: (i) cognitive and emotional processes, intelligent education, and motor learning; (ii) professional vision and collaborative learning; (iii) face-to-face social learning and real classroom learning; (iv) cognitive load and spatial learning; and (v) virtual reality-based learning, child learning, and technology-assisted special education. These trends illustrate a consistent growth in the use of portable technologies in education over the past 20 years and an emerging shift towards "naturalistic" approaches, with keywords such as "face-to-face" and "real-world" gaining prominence. These observations underscore the need to further generalize the current research to real-world classroom settings and call for interdisciplinary collaboration between researchers and educators. Also, combining multimodal technologies and conducting longitudinal studies will be essential for a comprehensive understanding of teaching and learning processes.},
}

@article {pmid40502712,
year = {2025},
author = {Chen, H and Zhang, M and Ye, T and Wolpert, MA and Ding, N},
title = {Low-frequency cortical activity reflects context-dependent parsing of word sequences.},
journal = {iScience},
volume = {28},
number = {6},
pages = {112650},
pmid = {40502712},
issn = {2589-0042},
abstract = {During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context-the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts.},
}

@article {pmid40501187,
year = {2025},
author = {Wang, YJ and Jie, Z and Liu, Y and Pan, Y},
title = {Dyad averaged BMI-dependent interbrain synchrony during continuous mutual prediction in social coordination.},
journal = {Social neuroscience},
volume = {},
number = {},
pages = {1-10},
doi = {10.1080/17470919.2025.2517068},
pmid = {40501187},
issn = {1747-0927},
abstract = {Obesity is linked to notable psychological risks, particularly in social interactions where individuals with high body mass index (BMI) often encounter stigmatization and difficulties in forming and maintaining social connections. Although awareness of these issues is growing, there is a lack of research on real-time, dynamic interactions involving dyads with various BMI levels. To address this gap, our study employed a joint finger-tapping task, where participant dyads engaged in coordinated activity while their brain activity was monitored using functional near-infrared spectroscopy (fNIRS). Our findings showed that both Bidirectional and Unidirectional Interaction conditions exhibited higher levels of behavioral and interbrain synchrony compared to the No Interaction condition. Notably, only in the Bidirectional Interaction condition, higher dyadic BMI was significantly correlated with poorer behavioral coordination and reduced interbrain synchrony. This finding suggests that the ability to maintain social coordination, particularly in scenarios requiring continuous mutual prediction and adjustment, is modulated by dyads' BMI.},
}

@article {pmid40499369,
year = {2025},
author = {Banovoth, RS and K V, K},
title = {Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.},
journal = {Computers in biology and medicine},
volume = {194},
number = {},
pages = {110397},
doi = {10.1016/j.compbiomed.2025.110397},
pmid = {40499369},
issn = {1879-0534},
abstract = {The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit rich neurodynamic features in the spatiotemporal domain, making them well-suited for processing brain signals, mainly those captured using the widely used non-invasive Electroencephalography (EEG) technique. However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task. To address the aforementioned challenge, the proposed study introduces a novel model that incorporates Roman Domination within a Spiking Neural Network (RDSNN), where Roman domination identifies the most highly correlated channels or nodes. These channels generate an appropriate threshold for spike generation in the signals, which are then classified using the SNN. The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b. The results demonstrate not only superior accuracy compared to State-Of-the-Art (SOTA) methods but also a 35% reduction in computation time, attributed to the application of Roman domination.},
}

@article {pmid40499342,
year = {2025},
author = {Silveira, I and Varandas, R and Gamboa, H},
title = {Cognitive Lab: A dataset of biosignals and HCI features for cognitive process investigation.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108863},
doi = {10.1016/j.cmpb.2025.108863},
pmid = {40499342},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Attention, cognitive workload/fatigue, and emotional states significantly influence learning outcomes, cognitive performance, and human-machine interactions. However, existing assessment methodologies fail to fully capture the multimodal nature of these cognitive processes, limiting their application in adaptive learning environments. This study presents the Cognitive Lab, a comprehensive multimodal dataset designed to investigate these cognitive processes across real-time learning scenarios. Specifically, it aims to capture and enable the classification of (1) attention and cognitive workload states using standard cognitive tasks, (2) cognitive fatigue arising from prolonged digital activities, and (3) emotional and learning states during interactive lessons.

METHODS: The Cognitive Lab dataset consists of three distinct subsets, each developed through specific experimental scenarios targeting different aspects of learning. Dataset 1 comprises recordings from eight participants performing N-Back and mental subtraction tasks, aimed at assessing attention and cognitive workload. Dataset 2 includes data from 10 participants engaged in a digital lesson, complemented by Corsi block-tapping and concentration tasks, to evaluate cognitive fatigue. Lastly, Dataset 3 captures data from 18 participants during an interactive Jupyter Notebook lesson, focusing on emotional states and learning processes. Each scenario combined biosignals (accelerometry, ECG, EDA, EEG, fNIRS, respiration) with Human-Computer Interaction (HCI) features (mouse-tracking, keyboard activity, screenshots). Machine learning models were applied to classify cognitive states, with cross-validation ensuring robust results.

RESULTS: The dataset enabled accurate classification of learning states, achieving up to 87% accuracy in differentiating learning states using mouse-tracking data. Furthermore, it successfully differentiated attention, cognitive workload, and cognitive fatigue states using biosignal and HCI data, with fNIRS, EEG, and ECG emerging as key contributors to classification performance. Variability across participants highlighted the potential for subject-specific calibration to enhance model accuracy.

CONCLUSIONS: The Cognitive Lab dataset represents a resource for investigating cognitive phenomena in real-world learning scenarios. Its integration of biosignals and HCI features enables the classification of cognitive states and supports advancements in adaptive learning systems, cognitive neuroscience, and brain-computer interface technologies.},
}

@article {pmid40498623,
year = {2025},
author = {Lian, Q and Wang, Y and Qi, Y},
title = {Dynamic Instance-level Graph Learning Network of Intracranial Electroencephalography Signals for Epileptic Seizure Prediction.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3578627},
pmid = {40498623},
issn = {2168-2208},
abstract = {Brain-computer interface (BCI) technology is emerging as a valuable tool for diagnosing and treating epilepsy, with deep learning-based feature extraction methods demonstrating remarkable progress in BCI-aided systems. However, accurately identifying causal relationships in temporal dynamics of epileptic intracranial electroencephalography (iEEG) signals remains a challenge. This paper proposes a Dynamic Instance-level Graph Learning Network (DIGLN) for seizure prediction using iEEG signals. The DIGLN comprises two core components: a grouped temporal neural network that extracts node features and a graph structure learning method to capture the causality from intra-channel to inter-channel. Furthermore, we propose a graphical interactive writeback technique to enable DIGLN to capture the causality from inter-channel to intra-channel. Consequently, our DIGLN enables patient-specific dynamic instance-level graph learning, facilitating the modelling of evolving signals and functional connectivities through end-to-end data-driven learning. Experimental results on the Freiburg iEEG dataset demonstrate the superior performance of DIGLN, surpassing other deep learning-based seizure prediction methods. Visualization results further confirm DIGLN's capability to learn interpretable and diverse connections.},
}

@article {pmid40496741,
year = {2025},
author = {Tiwari, N and Anwar, S and Bhattacharjee, V},
title = {EEG dataset for natural image recognition through visual stimuli.},
journal = {Data in brief},
volume = {60},
number = {},
pages = {111639},
pmid = {40496741},
issn = {2352-3409},
abstract = {Electroencephalography (EEG) is a technique for measuring the brain's electrical activity in the form of action potentials with electrodes placed on the scalp. Because of its non-invasive nature and ease of use, the approach is becoming increasingly popular for investigations. EEG reveals a wide spectrum of human brain potentials, such as event-related, sensory, and visually evoked potentials (VEPs), which aids in the development of intricate applications. Developing Apps or Brain-Computer Interface (BCI) devices demands data on these potentials. The present dataset comprises EEG recordings generated by thirty-two individuals in reaction to visual stimuli (VEPs). The rationale behind gathering this data is its ability to support EEG-based image classification and reconstruction while also advancing visual decoding. The primary purpose is to examine the cognitive processes behind both familiar and unfamiliar observations. A standardized experimental setup comprising many experimental phases was employed to capture the essence of the investigation and gather the dataset.},
}

@article {pmid40496017,
year = {2025},
author = {Reid, LV and Spalluto, CM and Wilkinson, TMA and Staples, KJ},
title = {Influenza-induced microRNA-155 expression is altered in extracellular vesicles derived from the COPD epithelium.},
journal = {Frontiers in cellular and infection microbiology},
volume = {15},
number = {},
pages = {1560700},
pmid = {40496017},
issn = {2235-2988},
mesh = {*MicroRNAs/genetics/metabolism ; Humans ; *Pulmonary Disease, Chronic Obstructive ; *Extracellular Vesicles/metabolism ; *Epithelial Cells/virology/metabolism ; *Influenza, Human/virology ; Cells, Cultured ; Gene Expression Profiling ; Cell Line ; },
abstract = {BACKGROUND: Influenza virus particularly affects those with chronic lung conditions such as Chronic Obstructive Pulmonary Disease (COPD). Airway epithelial cells are the first line of defense and primary target of influenza infection and release extracellular vesicles (EVs). EVs can transfer of biological molecules such as microRNAs (miRNAs) that can modulate the immune response to viruses through control of the innate and adaptive immune systems. The aim of this work was to profile the EV miRNAs released from bronchial epithelial cells in response to influenza infection and discover if EV miRNA expression was altered in COPD.

METHODS: Influenza infection of air-liquid interface (ALI) differentiated BCi-NS1.1 epithelial cells were characterized by analyzing the expression of antiviral genes, cell barrier permeability and cell death. EVs were isolated by filtration and size exclusion chromatography from the apical surface wash of ALI cultured bronchial epithelial cells. The EV miRNA cargo was sequenced and reads mapped to miRBase. The BCi sequencing results were further investigated by RT-qPCR and by using healthy and COPD primary epithelial cells.

RESULTS: Infection of ALI cultured BCi cells with IAV at 3.6 x 10[6] IU/ml for 24 h led to significant upregulation of anti-viral genes without high levels of cell death. EV release from ALI-cultured BCi cells was confirmed using electron microscopy and detection of known tetraspanin EV markers using western blot and the ExoView R100 platform. Differential expression analyses identified 5 miRNA that had a fold change of >0.6: miR-155-5p, miR-122-5p, miR-378a-3p, miR-7-5p and miR-146a-5p (FDR<0.05). Differences between EV, non-EV and cellular levels of these miRNA were detected. Primary epithelial cell release of EV and their miRNA cargo was similar to that observed for BCi. Intriguingly, miR-155 expression was decreased in EVs derived from COPD patients compared to EVs from control samples.

CONCLUSION: Epithelial EV miRNA release may be a key mechanism in modulating the response to IAV in the lungs. Furthermore, changes in EV miRNA expression may play a dysfunctional role in influenza-induced exacerbations of COPD. However, further work to fully characterize the function of EV miRNA in response to IAV in both health and COPD is required.},
}

*MicroRNAs/genetics/metabolism
Humans
*Pulmonary Disease, Chronic Obstructive
*Extracellular Vesicles/metabolism
*Epithelial Cells/virology/metabolism
*Influenza, Human/virology
Cells, Cultured
Gene Expression Profiling
Cell Line

@article {pmid40495523,
year = {2025},
author = {Gou, H and Bu, J and Cheng, Y and Liu, C and Gan, H and Liu, M and Zhao, Q and Chen, X and Ren, J and Hong, W and Wang, R and Cao, Y and Yu, C and Chen, X and Zhang, X},
title = {Improved Response Inhibition Through Cognition-Guided EEG Neurofeedback in Men With Methamphetamine Use Disorder.},
journal = {The American journal of psychiatry},
volume = {},
number = {},
pages = {appiajp20240475},
doi = {10.1176/appi.ajp.20240475},
pmid = {40495523},
issn = {1535-7228},
abstract = {OBJECTIVE: Impaired response inhibition is the core cognitive deficit in methamphetamine use disorder (MUD), and methamphetamine cue reactivity is a major factor that reduces inhibition efficiency. The authors sought to use cognition-guided neurofeedback to deactivate methamphetamine cue-related brain reactivity patterns in men with MUD to improve their response inhibition.

METHODS: A cognition-guided, closed-loop EEG-based neurofeedback protocol was employed. Methamphetamine cue-related brain activity patterns were identified offline using multivariate pattern analysis of EEG data from all channels during a methamphetamine cue reactivity task. In the real-time feedback phase, participants were trained to deactivate their methamphetamine cue-related patterns, which were presented as feedback. The study included two samples, totaling 99 men with MUD. In sample 1, 66 men received 10 neurofeedback sessions based either on their own brain activity patterns (real neurofeedback group 1, N=33) or on randomly matched participants' patterns (yoke neurofeedback group, N=33). Sample 2, which was used to validate findings in sample 1, included a real feedback group (real neurofeedback group 2; N=17) and a standard rehabilitation group (N=16) that received only standard rehabilitation without additional intervention. Response inhibition was assessed using a go/no-go task based on methamphetamine-related cues before and after the intervention.

RESULTS: Compared to the yoke feedback group, real neurofeedback group 1 successfully deactivated methamphetamine cue-related brain reactivity patterns, resulting in significantly enhanced response inhibition (d-prime, Cohen's f=0.31). Neurofeedback performance in real neurofeedback group 1 was significantly correlated with improved response inhibition. Additionally, response inhibition improvements could be predicted by initial neurofeedback performance and baseline characteristics. Sample 2 replicated these findings, showing that response inhibition in real neurofeedback group 2 was improved and predictable. Notably, these intervention effects in real neurofeedback group 2 were better than those in the standard rehabilitation group.

CONCLUSIONS: These findings underscore the efficacy of cognition-guided neurofeedback for treating MUD, thereby suggesting its potential applicability in other addiction interventions.},
}

@article {pmid40495436,
year = {2025},
author = {Rozovsky, R and Wolfe, M and Abdul-Waalee, H and Chobany, M and Malgireddy, G and Hart, JA and Lepore, B and Vahedifard, F and Phillips, ML and Birmaher, B and Skeba, A and Diler, RS and Bertocci, MA},
title = {Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies.},
journal = {Brain and behavior},
volume = {15},
number = {6},
pages = {e70589},
doi = {10.1002/brb3.70589},
pmid = {40495436},
issn = {2162-3279},
support = {R01-MH-121451/MH/NIMH NIH HHS/United States ; },
mesh = {Humans ; Adolescent ; *Bipolar Disorder/diagnostic imaging/pathology ; Male ; Female ; *Gray Matter/diagnostic imaging/pathology ; Inpatients ; Magnetic Resonance Imaging/methods ; Machine Learning ; Support Vector Machine ; *Brain/pathology/diagnostic imaging ; *Mental Disorders/diagnostic imaging ; },
abstract = {BACKGROUND: Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole-brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis.

METHODS: Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13-17 with BD-I/II (n = 34), other specified BD (OSB) (n = 106), other non-bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms.

RESULTS: Whole-brain classifiers in the model BD-I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self-reported mania, negative affect, or anxiety were observed in all inpatient groups.

CONCLUSIONS: These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well-characterized BD-I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.},
}

Humans
Adolescent
*Bipolar Disorder/diagnostic imaging/pathology
Male
Female
*Gray Matter/diagnostic imaging/pathology
Inpatients
Magnetic Resonance Imaging/methods
Machine Learning
Support Vector Machine
*Brain/pathology/diagnostic imaging
*Mental Disorders/diagnostic imaging

@article {pmid40494420,
year = {2025},
author = {Spinelli, R and Sanchís, I and Siano, A},
title = {Fighting Alzheimer's naturally: Peptides as multitarget drug leads.},
journal = {Bioorganic & medicinal chemistry letters},
volume = {},
number = {},
pages = {130305},
doi = {10.1016/j.bmcl.2025.130305},
pmid = {40494420},
issn = {1464-3405},
abstract = {In this review, we provide a comprehensive analysis of the role of natural peptides-particularly those derived from amphibian skin secretions-as multitarget-directed ligands (MTDLs) in the context of Alzheimer's disease (AD). Given the multifactorial nature of AD, where cholinergic dysfunction intersects with amyloid-β aggregation, tau hyperphosphorylation, oxidative stress, metal ion imbalance, and monoamine oxidase dysregulation, therapeutic strategies capable of modulating several pathological pathways simultaneously are urgently needed. We begin by revisiting the cholinergic hypothesis and its molecular and structural underpinnings, emphasizing the relevance of key binding sites such as the catalytic active site (CAS) and the peripheral anionic site (PAS) of cholinesterases. The central axis of this review lies in the exploration of naturally occurring peptides that have demonstrated dual or multiple activities against AD-related targets. We highlight our group's pioneering work on amphibian-derived peptides such as Hp-1971, Hp-1935, and BcI-1003, which exhibit non-competitive inhibition of AChE and BChE, MAO-B modulation, and antioxidant properties. Furthermore, we describe additional peptide-rich extracts and bioactive sequences from various amphibians and other animal or plant sources, expanding the landscape of natural molecules with neuroprotective potential. We also delve into peptide modification strategies-such as amino acid substitution, cyclization, D-amino acid incorporation, and terminal/side-chain functionalization-that have been employed to enhance peptide stability, blood-brain barrier permeability, and target affinity. These strategies not only improve the pharmacokinetic profiles of native peptides but also open the door for the rational design of next-generation peptide therapeutics. Overall, this review underscores the vast potential of natural peptides as scaffolds for the development of multifunctional agents capable of intervening in the complex cascade of Alzheimer's pathology.},
}

@article {pmid40494387,
year = {2025},
author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ},
title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade344},
pmid = {40494387},
issn = {1741-2552},
abstract = {OBJECTIVE: Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.

APPROACH: The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.

RESULTS: The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.

SIGNIFICANCE: These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.},
}

@article {pmid40494367,
year = {2025},
author = {Thielen, J},
title = {Addressing BCI inefficiency in c-VEP-based BCIs: A comprehensive study of neurophysiological predictors, binary stimulus sequences, and user comfort.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ade316},
pmid = {40494367},
issn = {2057-1976},
abstract = {This study investigated the presence of brain-computer interface (BCI) inefficiency in BCIs using the code-modulated visual evoked potential (c-VEP). It further explored neurophysiological predictors of performance variability and evaluated a wide range of binary stimulus sequences in terms of classification accuracy and user comfort, aiming to identify strategies to mitigate c-VEP BCI inefficiency. Approach. In a comprehensive empirical analysis, ten different binary stimulus sequences were offline evaluated. These sequences included five code families (m-sequence, de Bruijn sequence, Golay sequence, Gold code, and a Gold code set), each in original and modulated form. To identify predictors of performance variability, resting-state alpha activity, heart rate and heart rate variability, sustained attention, and flash-VEP characteristics were studied. Main Results. Results confirmed substantial inter-individual variability in c-VEP BCI efficiency. While all participants reached a near-perfect classification accuracy, their obtained speed varied substantially. Four flash-VEP features were found to significantly correlate with the observed performance varibility: the N2 latency, the P2 latency and amplitude, and the N3 amplitude. Among the tested stimulus conditions, the m-sequence emerged as the best-performing universal stimulus. However, tailoring stimulus selection to individuals led to significant improvements in performance. Cross-decoding was successful between modulated stimulus conditions, but showed challenges when generalizing across other stimulus conditions. Lastly, while overall comfort ratings were comparable across conditions, stimulus modulation was associated with a significant decrease in user comfort. Significance. This study challenges the assumption of universal efficiency in c-VEP BCIs. The findings highlight the importance of accounting for individual neurophysiological differences and underscore the need for personalized stimulus protocols and decoding strategies to enhance both performance and user comfort.},
}

@article {pmid40493465,
year = {2025},
author = {Rabiee, A and Ghafoori, S and Cetera, A and Norouzi, M and Besio, W and Abiri, R},
title = {A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3578235},
pmid = {40493465},
issn = {1558-2531},
abstract = {This study aims to enhance brain-computer interface (BCI) applications for individuals with motor impairments by comparing the effectiveness of noninvasive tripolar concentric ring electrode electroencephalography (tEEG) with conventional electroencephalography (EEG) technology. The goal is to determine which EEG technology is more effective in measuring and decoding different grasp-related neural signals. The approach involves experimenting on ten healthy participants who performed two distinct reach-and-grasp movements: power grasp and precision grasp, with a no-movement condition serving as the baseline. Our research compares EEG and tEEG in decoding grasping movements, focusing on signal-to-noise ratio (SNR), spatial resolution, and wavelet time-frequency analysis. Additionally, our study involved extracting and analyzing statistical features from the wavelet coefficients, and both binary and multiclass classification methods were employed. Four machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)-were used to evaluate the decoding accuracies. Our results indicated that tEEG demonstrated higher quality performance compared to conventional EEG in various aspects. This included a higher signal-to-noise ratio and improved spatial resolution. Furthermore, wavelet timefrequency analyses validated these findings, with tEEG exhibiting increased power spectra, thus providing a more detailed and informative representation of neural dynamics. The use of tEEG led to significant improvements in decoding accuracy for differentiating grasp movement types. Specifically, tEEG achieved around 90.00% accuracy in binary and 75.97% for multiclass classification. These results exceed those from conventional EEG, which recorded a maximum of 77.85% and 61.27% in similar tasks, respectively.},
}

@article {pmid40493186,
year = {2025},
author = {Alawieh, H and Liu, D and Madera, J and Kumar, S and Racz, FS and Fey, AM and Del R Millán, J},
title = {Electrical spinal cord stimulation promotes focal sensorimotor activation that accelerates brain-computer interface skill learning.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {24},
pages = {e2418920122},
doi = {10.1073/pnas.2418920122},
pmid = {40493186},
issn = {1091-6490},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Adult ; *Learning/physiology ; *Spinal Cord Injuries/physiopathology/rehabilitation ; Female ; *Spinal Cord Stimulation/methods ; *Motor Cortex/physiology ; Young Adult ; *Motor Skills/physiology ; },
abstract = {Injuries affecting the central nervous system may disrupt neural pathways to muscles causing motor deficits. Yet the brain exhibits sensorimotor rhythms (SMRs) during movement intents, and brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. However, noninvasive BCIs suffer from the instability of SMRs, requiring longitudinal training for users to learn proper SMR modulation. Here, we accelerate this skill learning process by applying cervical transcutaneous electrical spinal stimulation (TESS) to inhibit the motor cortex prior to longitudinal upper-limb BCI training. Results support a mechanistic role for cortical inhibition in significantly increasing focality and strength of SMRs leading to accelerated BCI control in healthy subjects and an individual with spinal cord injury. Improvements were observed following only two TESS sessions and were maintained for at least one week in users who could not otherwise achieve control. Our findings provide promising possibilities for advancing BCI-based motor rehabilitation.},
}

Humans
*Brain-Computer Interfaces
Male
Adult
*Learning/physiology
*Spinal Cord Injuries/physiopathology/rehabilitation
Female
*Spinal Cord Stimulation/methods
*Motor Cortex/physiology
Young Adult
*Motor Skills/physiology

@article {pmid40490658,
year = {2025},
author = {Norizadeh Cherloo, M and Kashefi Amiri, H and Mijani, AM and Zhan, L and Daliri, MR},
title = {A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.},
journal = {Behavior research methods},
volume = {57},
number = {7},
pages = {196},
pmid = {40490658},
issn = {1554-3528},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Signal-To-Noise Ratio ; *Evoked Potentials, Visual/physiology ; *Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Recently, SSVEP-based brain-computer interfaces (BCIs) have received increasing attention from researchers due to their high signal-to-noise ratios (SNR), high information transfer rates (ITR), and low user training. Therefore, various methods have been proposed to recognize the frequency of SSVEPs. This paper reviewed the state-of-the-art frequency detection methods in SSVEP-based BCIs. Nineteen multi-channel SSVEP detection methods, organized into four categories based on different analytical approaches, were studied. All methods are template-based approaches and classified into four groups according to the basic models they employ: canonical correlation analysis (CCA), multivariate synchronization index (MSI), task-related component analysis (TRCA), and correlated component analysis (CORRCA). Each group consists of methods that use one of these basic models as the core model for their approach. This paper provides a description, a clear flowchart, and MATLAB code for each method and helps researchers use or develop the existing SSVEP detection methods. Although all methods were evaluated in separate studies, a comprehensive comparison of methods is still missing. In this study, several experiments were conducted to assess the performance of SSVEP detection methods. The benchmark 40-class SSVEP dataset from 35 subjects was used to evaluate methods. All methods were applied to the dataset and were evaluated in terms of classification accuracy, information transfer rate (ITR), and computational time. The experiment results revealed that four factors efficiently design an accurate, robust SSVEP detection method. (1) employing filter bank analysis to incorporate fundamental and harmonics frequency components, (2) utilizing calibration data to construct optimized reference signals, (3) integrating spatial filters of all stimuli to construct classification features, and (4) calculating spatial filters using training trials. Furthermore, results showed that filter bank ensemble task-related components (FBETRCA) achieved the highest performance.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
Signal-To-Noise Ratio
*Evoked Potentials, Visual/physiology
*Signal Processing, Computer-Assisted
Algorithms

@article {pmid40490007,
year = {2025},
author = {Chen, Y and Peng, Y and Tang, J and Camilleri, TA and Camilleri, KP and Kong, W and Cichocki, A},
title = {EEG-based affective brain-computer interfaces: recent advancements and future challenges.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade290},
pmid = {40490007},
issn = {1741-2552},
abstract = {As one of the most popular brain-computer interface (BCI) paradigms, affective BCI (aBCI) decodes the human emotional states from brain signals and imposes necessary feedback to achieve neural regulation when negative emotional states (i.e., depression, anxiety) are detected, which are considered as the two basic functions of aBCI system. Electroencephalogram (EEG) is the scalp reflection of neural activities and has been regarded as the gold standard of emotional effects. Recently, rapid progresses have been made for emotion recognition and regulation with the purpose of constructing a high-performance closed-loop EEG-based aBCI system. Therefore, it is necessary to make a timely review for aBCI research by summarizing the current progresses as well as challenges and opportunities, to draw the attention from both academia and industry. Toward this goal, a systematic literature review was performed to summarize not only the recent progresses in emotion recognition and regulation from the perspective of closed-loop aBCI, but also the main challenges and future research focuses to narrow the gap between the current research and real applications of aBCI systems. Approach. A systematic literature review on EEG-based emotion recognition and regulation was performed on Web of Science and related databases, resulting in more than 100 identified studies. These studies were analyzed according to the experimental paradigm, emotion recognition methods in terms of different scenarios, and the applications of emotion recognition in diagnosis and regulation of affective disorders. Main results. Based on the literature review, advancements for EEG-based aBCI research were extensively summarized from six aspects including the 'emotion elicitation paradigms and data sets', 'inner exploration of EEG information','outer extension of fusing EEG with other data modalities', 'cross-scene emotion recognition', 'emotion recognition by considering real scenarios', and 'diagnosis and regulation of affective disorders'. In addition, future opportunities were concluded by focusing on the main challenges in hindering the aBCI system to move from laboratory to real applications. Moreover, the neural mechanisms and theoretical basis behind EEG emotion recognition and regulation are also introduced to provide support for the advancements and challenges in aBCI. Significance. This review summarizes the current practices and performance outcomes in emotion recognition and regulation. Future directions in response to the existing challenges are provided with the expectation of guiding the aBCI research to focus on the necessary key technologies of aBCI system in practical deployment.},
}

@article {pmid40490003,
year = {2025},
author = {Tates, A and Matran-Fernandez, A and Halder, S and Daly, I},
title = {Speech imagery brain-computer interfaces: a systematic literature review.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade28e},
pmid = {40490003},
issn = {1741-2552},
abstract = {Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whose sensations are similar to their non-imagery counterparts. SI underpins language processes and may have similar building blocks to overt speech without the final articulatory outcome. The kinesthetic experience of SI has been proposed to be a projection of the expected articulatory outcome in a top-down processing manner. As SI seems to be a core human cognitive task it has been proposed as a paradigm for Brain Computer Interfaces (BCI). One important aspect of BCI designs is usability, and SI may present an intuitive paradigm, which has brought the attention of researchers to attempt to decode SI from brain signals. In this paper we review the important aspects of SI-BCI decoding pipelines. \textit{Approach}.
We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we filtered peer-reviewed reports via a search of Google Scholar and PubMed. We selected a total of 104 reports that attempted to decode Speech Imagery from neural activity. \textit{Main results}.
Our review reveals a growing interest in SI decoding in the last 20 years, and shows how different neuroimaging modalities have been employed to record SI in distinct ways to instruct participants to perform this task. We discuss the signal processing methods used along with feature extraction techniques and found a high preference for Deep Learning models. We have summarized and compared the decoding attempts by quantifying the efficacy of decoding by measuring Information Transfer Rates. Notably, fewer than 6\% of studies reported real-time decoding, with the vast majority focused on offline analyses. This suggests existing challenges of this paradigm, as the variety of approaches and outcomes prevents a clear identification of the field's current state-of-the-art. We offer a discussion of future research directions. \textit{Significance}
Speech Imagery is an attractive BCI paradigm. This review outlines the increasing interest in SI, the methodological trends, the efficacy of different approaches, and the current progress toward real-time decoding systems.},
}

@article {pmid40489280,
year = {2025},
author = {Wang, Z and Zhang, Y and Zhang, Z and Xie, SQ and Lanzon, A and Heath, WP and Li, Z},
title = {Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3577813},
pmid = {40489280},
issn = {2168-2208},
abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-related component analysis (iTRCA), which leverages knowledge from source subjects while considering their individual contributions. iTRCA extracts two types of features: (1) the subject-general feature, capturing shared information between source and target subjects in a common latent space, and (2) the subject-specific feature, preserving the unique characteristics of the target subject. To mitigate negative transfer, we further design an enhanced framework, subject selection-based iTRCA (SS-iTRCA), which integrates a similarity-based subject selection strategy to identify appropriate source subjects for transfer based on their task-related components (TRCs). Comparative evaluations on the Benchmark, BETA, and a self-collected dataset demonstrate the effectiveness of the proposed iTRCA and SS-iTRCA frameworks. This study provides a potential solution for developing high-performance SSVEP-based BCIs with reduced target subject data.},
}

@article {pmid40485770,
year = {2025},
author = {Tyler, WJ and Adavikottu, A and Blanco, CL and Mysore, A and Blais, C and Santello, M and Unnikrishnan, A},
title = {Neurotechnology for enhancing human operation of robotic and semi-autonomous systems.},
journal = {Frontiers in robotics and AI},
volume = {12},
number = {},
pages = {1491494},
pmid = {40485770},
issn = {2296-9144},
abstract = {Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness.},
}

@article {pmid40398443,
year = {2025},
author = {Jehn, C and Kossmann, A and Katerina Vavatzanidis, N and Hahne, A and Reichenbach, T},
title = {CNNs improve decoding of selective attention to speech in cochlear implant users.},
journal = {Journal of neural engineering},
volume = {22},
number = {3},
pages = {},
doi = {10.1088/1741-2552/addb7b},
pmid = {40398443},
issn = {1741-2552},
mesh = {Humans ; *Cochlear Implants ; *Attention/physiology ; *Speech Perception/physiology ; Female ; Male ; Electroencephalography/methods ; Middle Aged ; Adult ; *Neural Networks, Computer ; Aged ; Acoustic Stimulation/methods ; Support Vector Machine ; },
abstract = {Objective. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory attention decoding (AAD) aims to decode the target speaker of a listener from electroencephalography (EEG), and then use this information to steer an auditory prosthesis towards this speech signal. In normal-hearing individuals, deep neural networks (DNNs) have been shown to improve AAD compared to simpler linear models. We aim to demonstrate that DNNs can improve attention decoding in CI users too, which would make them the state-of-the-art candidate for a neuro-steered CI.Approach. To this end, we first collected an EEG dataset on selective auditory attention from 25 bilateral CI users, and then implemented both a linear model as well as a convolutional neural network (CNN) for attention decoding. Moreover, we introduced a novel, objective CI-artifact removal strategy and evaluated its impact on decoding accuracy, alongside learnable speaker classification using a support vector machine (SVM).Main results. The CNN outperformed the linear model across all decision window sizes from 1 to 60 s. Removing CI artifacts modestly improved the CNN's decoding accuracy. With SVM classification, the CNN decoder reached a peak mean decoding accuracy of 74% at the population level for a 60 s decision window.Significance. These results demonstrate the superior potential of CNN-based decoding for neuro-steered CIs, which could improve speech perception of its users in cocktail party situations significantly.},
}

Humans
*Cochlear Implants
*Attention/physiology
*Speech Perception/physiology
Female
Male
Electroencephalography/methods
Middle Aged
Adult
*Neural Networks, Computer
Aged
Acoustic Stimulation/methods
Support Vector Machine

@article {pmid40484831,
year = {2025},
author = {Zhao, JZ},
title = {[A historical review and future outlook of neurosurgery in China].},
journal = {Zhonghua yi xue za zhi},
volume = {105},
number = {21},
pages = {1679-1685},
doi = {10.3760/cma.j.cn112137-20250325-00727},
pmid = {40484831},
issn = {0376-2491},
mesh = {*Neurosurgery/trends/history ; China ; Humans ; History, 20th Century ; History, 21st Century ; Societies, Medical ; Artificial Intelligence ; },
abstract = {Since its inception in the early 20th century at Peking Union Medical College Hospital, neurosurgery in China has gone through a century-long process from its initial establishment, development to modernization, forming a complete system, covering vascular diseases, tumors, epilepsy, and other diseases. This article reviews the key pioneers and historical milestones in Chinese neurosurgery, highlights the founding and advancement of the Society of Neurosurgery of Chinese Medical Association, and shows major achievements in standardization, training, and international cooperation, etc. At present, with the application of technologies such as artificial intelligence and brain-computer interfaces, network-based neurosurgery has emerged and developed rapidly, marking the transition to Neurosurgery 4.0. In the future, Chinese neurosurgery is poised to further promote interdisciplinary integration and clinical translation in support of the high-quality development of brain science.},
}

*Neurosurgery/trends/history
China
Humans
History, 20th Century
History, 21st Century
Societies, Medical
Artificial Intelligence

@article {pmid40483841,
year = {2025},
author = {Zakrzewski, S and Stasiak, B and Wojciechowski, A},
title = {Supervised factor selection in tensor decomposition of EEG signal.},
journal = {Computer methods and programs in biomedicine},
volume = {269},
number = {},
pages = {108866},
doi = {10.1016/j.cmpb.2025.108866},
pmid = {40483841},
issn = {1872-7565},
abstract = {BACKGROUND AND OBJECTIVE: Tensor decomposition methods are important tools for multidimensional data analysis, which have also proved useful for EEG signal processing. However, their direct application for tasks involving supervised training, such as EEG data classification in systems based on brain-computer interfaces, is limited by the inherently unsupervised nature of the optimization algorithms used for tensor factorization.

METHODS: In this work, we propose a solution for a motor imagery classification task based on parallel factor analysis (PARAFAC) of EEG data. The individual factors obtained through PARAFAC decomposition are subjected to statistical analysis, enabling us to select signal components relevant to the classification problem. To choose the rank of the decomposition, we propose a special score function based on cosine similarity of all possible pairs of decompositions.

RESULTS: The proposed method was shown to significantly increase the classification accuracy in the case of the best-performing subjects, when provided with an EEG signal satisfying certain conditions, such as sufficient trial length. Besides, representation of the statistically significant components in the form of a heatmap, defined over the space-frequency plane, proved suitable for direct interpretation in the context of event-related synchronization/desynchronization of cortical activity.

CONCLUSION: The proposed approach, joining universal tensor decomposition methods with statistical evaluation of the obtained components, has the potential to yield high accuracy and explainability of the results while significantly reducing the input space dimensionality.},
}

@article {pmid40483616,
year = {2025},
author = {Han, J and Zhan, G and Wang, L and Liang, D and Zhang, H and Zhang, L and Kang, X},
title = {Decoding EEG-based cognitive load using fusion of temporal and functional connectivity features.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2025.2514132},
pmid = {40483616},
issn = {1476-8259},
abstract = {Evaluating cognitive load using electroencephalogram (EEG) signals is a crucial research area in the field of Brain-Computer Interfaces (BCI). However, achieving high accuracy and generalization in feature extraction and classification for cognitive load assessment remains a challenge, primarily due to the low signal-to-noise ratio of EEG signals and the inter-individual variability in EEG data. In this study, we propose a novel deep learning architecture that integrates temporal information features and functional connectivity features to enhance EEG signal analysis. Functional connectivity features capture inter-channel information, while temporal features are extracted from continuous signal segments using a Long Short-Term Memory (LSTM) network enhanced with an attention mechanism. The fusion strategy combines these two information streams to leverage their complementary strengths, resulting in improved classification performance. We evaluated our architecture on two publicly available datasets, and the results demonstrate its robustness in cognitive load recognition. Achieving performance comparable to the best existing methods on two public datasets. Ablation studies further substantiate the contributions of each module, demonstrating the importance of combining temporal and functional connectivity features for optimal results. These findings underscore the robustness and versatility of the proposed approach, paving the way for more effective EEG-based BCI applications.},
}

@article {pmid40482972,
year = {2025},
author = {M V, H and K, K and B, SB},
title = {An EEG-based imagined speech recognition using CSP-TP feature fusion for enhanced BCI communication.},
journal = {Behavioural brain research},
volume = {},
number = {},
pages = {115652},
doi = {10.1016/j.bbr.2025.115652},
pmid = {40482972},
issn = {1872-7549},
abstract = {BACKGROUND: Imagined speech has emerged as a promising paradigm for intuitive control of brain-computer interface (BCI)-based communication systems, providing a means of communication for individuals with severe brain disabilities. In this work, a non-invasive electroencephalogram (EEG)-based automated imagined speech recognition model was proposed to assist communication to convey the individual's intentions or commands. The proposed approach uses Common Spatial Patterns (CSP) and Temporal Patterns (TP) for feature extraction, followed by feature fusion to capture both spatial and temporal dynamics in EEG signals. This fusion of the CSP and TP domains enhances the discriminative power of the extracted features, leading to improved classification accuracy.

NEW METHOD: An EEG data set was collected from 15 subjects while performing an imagined speech task with a set of ten words that are more suitable for paralyzed patients. The EEG signals were preprocessed and a set of statistical characteristics was extracted from the fused CSP and TP domains. Spectral analysis of the signals was performed with respect to ten imagined words to identify the underlying patterns in EEG. Machine learning models, including Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), were employed for pairwise and multiclass classification.

RESULTS: The proposed model achieved average classification accuracies of 83.83% ± 5.94 and 64.58% ± 10.43 and maximum accuracies of 97.78% and 79.22% for pairwise and multiclass classification, respectively. These results demonstrate the effectiveness of the CSP-TP feature fusion approach, outperforming existing state-of-the-art methods in imagined speech recognition.

CONCLUSION: The findings suggest that EEG-based automatic imagined speech recognition (AISR) using CSP and TP techniques has significant potential for use in BCI-based assistive technologies, offering a more natural and intuitive means of communication for individuals with severe communication limitations.},
}

@article {pmid40481499,
year = {2025},
author = {Zhang, N and Huang, Z and Xia, Y and Tao, S and Wu, T and Sun, S and Zhu, Y and Jiang, G and Lu, X and Gao, Y and Guo, F and Cao, R and Chen, S and Zhang, L and Zou, X and Chen, M and Zhang, G},
title = {Remote ischemia precondition protects against renal IRI through apoptosis associated vesicles carrying MIF protein via modulating DUSP6/JNK signaling axis.},
journal = {Journal of nanobiotechnology},
volume = {23},
number = {1},
pages = {422},
pmid = {40481499},
issn = {1477-3155},
support = {tsgn202103116//Tai-Shan Scholar Program from Shandong Province/ ; 81900618//the National Natural Science Foundation of China/ ; 2023GX026//the Program of Scientific and Technological Development of Weifang/ ; GSP-LCYJFH11//Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds/ ; 2023YXZDXK02//Jiangsu Provincial Key Discipline and Laboratory Construction Funds of Urology/ ; CZXM-ZK-47//National clinical key discipline construction funds/ ; 202305033//Nanjing Key Science and Technology Special Project (Life and Health) - Medical-Engineering Collaborative Project/ ; 82100732//Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Remote ischemic preconditioning (rIPC) has been reported to protect against kidney ischemia-reperfusion injury (IRI) through the delivery of extracellular vesicles (EVs). Among these, apoptosis-induced compensatory proliferation signaling-related vesicles (ACPSVs) can transmit proliferation signals to surrounding cells. However, the underlying mechanisms remain unclear. This study aimed to investigate the role of ACPSVs in renal IRI following rIPC and to elucidate the associated mechanisms.

RESULTS: We demonstrated that rIPC plasma or ACPSVs alleviated renal damage and inflammation, with the protective effects abolished upon the removal of ACPSVs from the plasma. EVs isolated via differential centrifugation exhibited defining characteristics of ACPSVs. Co-culture experiments revealed that ACPSVs reduced apoptosis and enhanced the viability of HK-2 cells under hypoxia/reoxygenation (H/R) conditions. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses highlighted the critical role of macrophage migration inhibitory factor (MIF) protein in ACPSVs. Using CRISPR/Cas9 technology, we generated MIF-knockout HeLa cells to induce the production of MIF-deficient ACPSVs. The protective effects of ACPSVs were significantly attenuated when MIF was knocked out. Transcriptome sequencing and chromatin immunoprecipitation (ChIP) assays revealed that MIF suppresses dual-specificity phosphatase 6 (DUSP6) expression by promoting H3K9 trimethylation (H3K9me3) in the DUSP6 promoter region, thereby activating the JNK signaling pathway. In rescue experiments, treatment with the DUSP6 inhibitor BCI effectively restored the protective function of MIF-deficient ACPSVs.

CONCLUSION: This study underscores the protective role of ACPSVs derived from rIPC-treated rats and serum-starved cells against renal IRI through the MIF/DUSP6/JNK signaling axis, offering a potential clinical therapeutic strategy for acute kidney injury induced by IRI.

GRAPHICAL ABSTRACT: [Image: see text]

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12951-025-03505-9.},
}

@article {pmid40481295,
year = {2025},
author = {Zheng, J and Yu, J and Xu, M and Guan, C and Fu, Y and Shen, M and Chen, H},
title = {Expectation violation enhances short-term source memory.},
journal = {Psychonomic bulletin & review},
volume = {},
number = {},
pages = {},
pmid = {40481295},
issn = {1531-5320},
abstract = {Recent studies of short-term source amnesia demonstrated that source information is rapidly forgotten in memory, reflecting a highly selective mode of memory encoding. In this study, we explored the flexibility of memory selection by investigating whether short-term source amnesia is affected by expectation violations. In seven experiments, we first replicated the short-term source amnesia phenomenon and then induced various forms of expectation violations. The results consistently showed that the short-term source amnesia was significantly reduced or attenuated when expectation violation occurred, indicating a strengthening effect of expectation violation on short-term source memory. This effect occurred quite quickly, nearly at the same time as the occurrence of unexpected events. Moreover, the source memory was improved even when the unexpected events were completely irrelevant to the task set or target stimuli. These findings suggest that short-term memory tends to encode and maintain more detailed source information when encountering expectation violations, which might be an adaptive mechanism for handling unexpected environmental changes.},
}

@article {pmid40481078,
year = {2025},
author = {Peng, L and Wang, L and Wu, S and Gu, M and Deng, S and Liu, J and Cheng, CK and Sui, X},
title = {Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {19938},
pmid = {40481078},
issn = {2045-2322},
support = {No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 2022ZD0208601//the STI 2030-Major Projects/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; No. 62176158//the National Natural Science Foundation of China/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; WH410362603/001//the STAR Project of Shanghai Jiao Tong University/ ; },
mesh = {Finite Element Analysis ; *Electrodes, Implanted ; Biomechanical Phenomena ; Microelectrodes ; Brain/physiology ; Humans ; Stress, Mechanical ; Brain-Computer Interfaces ; },
abstract = {Neural electrodes are widely used in brain-computer interfaces and neuroprosthesis for the treatment of various neurological disorders. However, as components that come into direct contact with neural tissue, implanted neural electrodes could cause mechanical damage during surgical insertions or while inside the brain. Thus, accurately and timely assessing this damage was vital for chronic implantation, which posed a significant challenge. This study aimed to evaluate the biomechanical effects and clinical application risks of a polyimide-based ultrathin flexible intracortical microelectrode through the finite element method (FEM). It analyzed the electrode-brain biomechanical effects during the electrode's insertion process and under steady-state acceleration with the electrode inside the brain. Furthermore, the study examined the impact of factors including implantation depth (ranging from 5 to 5000 μm), cortical thickness (0.5 mm, 2.5 mm, and 4.5 mm), step displacement (from 1 to 5 μm) during insertion, and acceleration direction (vertical and horizontal) on the electrode's biomechanical effects. The primary findings showed that the 98th percentile Von Mises Strain (ε98) and Von Mises Stress (σ98) in the region of interest (ROI) decreased dual-exponentially with increasing implantation depth and increased linearly with larger step displacements. Compared to the Von Mises strain threshold of 14.65%, as proposed by Sahoo et al., indicating a 50% risk of diffuse axonal injury (DAI), it was recommended to limit the initial step displacement during insertion to 1 μm, increasing to 5 μm at deeper locations (over 500 μm) to balance safety and efficiency. Additionally, it was found that cortical thickness had a negligible impact during insertion and while experiencing steady-state acceleration in vivo, with the three fitted curves almost coinciding when cortical thicknesses were 0.5 mm, 2.5 mm, and 4.5 mm. The flexible electrode exhibited excellent mechanical performance under steady-state acceleration in vivo, with ε98 being less than 0.3% and σ98 being less than 50 Pa, although it was more sensitive to horizontal acceleration. Thus, it could be concluded that during long-duration accelerations from transportation modes such as elevators and high-speed trains, the electrode's mechanical effects on brain tissue could be neglected, demonstrating long-term mechanical stability. This research was significant for guiding surgical insertion and clinical applications of flexible electrodes.},
}

Finite Element Analysis
*Electrodes, Implanted
Biomechanical Phenomena
Microelectrodes
Brain/physiology
Humans
Stress, Mechanical
Brain-Computer Interfaces

@article {pmid40481044,
year = {2025},
author = {Yi, W and Chen, J and Wang, D and Hu, X and Xu, M and Li, F and Wu, S and Qian, J},
title = {A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {953},
pmid = {40481044},
issn = {2052-4463},
support = {12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62006014//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12275295//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
mesh = {Humans ; *Electroencephalography ; Spectroscopy, Near-Infrared ; Brain-Computer Interfaces ; *Upper Extremity/physiology ; *Imagination ; *Joints/physiology ; Movement ; },
abstract = {As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.},
}

Humans
*Electroencephalography
Spectroscopy, Near-Infrared
Brain-Computer Interfaces
*Upper Extremity/physiology
*Imagination
*Joints/physiology
Movement

@article {pmid40480870,
year = {2025},
author = {Gunda, NK and Khalaf, MI and Bhatnagar, S and Quraishi, A and Gudala, L and Venkata, AKP and Alghayadh, FY and Alsubai, S and Bhatnagar, V},
title = {Retraction notice to "Lightweight attention mechanisms for EEG emotion recognition for brain computer interface".},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110502},
doi = {10.1016/j.jneumeth.2025.110502},
pmid = {40480870},
issn = {1872-678X},
}

@article {pmid40457516,
year = {2025},
author = {Zhang, Y and Deng, X and Wang, S and Zhou, W and Wu, Z and Tang, X and Lee, HJ and Zhang, D},
title = {High-Specificity Spatiotemporal Cholesterol Detection by Quadrature Phase-Shifted Polarization Stimulated Raman Imaging.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {},
number = {},
pages = {e202505038},
doi = {10.1002/anie.202505038},
pmid = {40457516},
issn = {1521-3773},
support = {2024YFA1408900//National Key Research and Development Program of China/ ; 82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities of China/ ; },
abstract = {Visualizing cholesterol dynamics in living systems in situ remains a fundamental challenge in biomedical imaging. Although fluorescence microscopy requires bulky tags that perturb small molecule behavior, stimulated Raman scattering (SRS) microscopy enables label-free detection of CH-rich molecules. However, conventional SRS probes only polarized Raman components, limiting molecular specificity by seemingly overlapped peaks. Here, we extend SRS microscopy to achieve rapid, comprehensive detection of Raman tensor through quadrature phase-shifted polarization SRS (QP[2]-SRS) microscopy. This technique exploits the underlying molecular signatures by detecting both polarized and depolarized components of third-order nonlinear susceptibility χ[(3)] that originates from molecular structural features. We adopt a specialized optical delay line that rapidly alternates between parallel- and perpendicular-polarization states. QP[2]-SRS enables unprecedented distinction of similar molecular species in complex mixtures, demonstrating approximately 10× enhancement in chemical specificity and 5× improvement in analytical accuracy. This enhanced sensitivity enables real-time monitoring of lipid dynamics in living C. elegans and reveals component heterogeneity and morphological changes of LD in NAFLD livers. QP[2]-SRS creates new opportunities for investigating cholesterol-dependent biological processes in their native environment, with broad potential for chemical imaging with enhanced molecular specificity.},
}

@article {pmid40480308,
year = {2025},
author = {Zhang, T and Jia, Y and Wang, N and Chai, X and He, Q and Cao, T and Mu, Q and Lan, Q and Zhao, J and Yang, Y},
title = {Recent advances in potential mechanisms of epidural spinal cord stimulation for movement disorders.},
journal = {Experimental neurology},
volume = {},
number = {},
pages = {115330},
doi = {10.1016/j.expneurol.2025.115330},
pmid = {40480308},
issn = {1090-2430},
abstract = {BACKGROUND: Epidural spinal cord stimulation (eSCS) has emerged as a promising neuromodulation technique for treating movement disorders. The underlying mechanisms of eSCS are still being explored, making it a compelling area for further research.

OBJECTIVE: This review aims to provide a comprehensive analysis of the mechanisms of eSCS, its stimulation parameters, and its clinical applications in movement disorders. It seeks to synthesize the current understanding of how eSCS interacts with the central nervous system to enhance motor function and promotes neural plasticity for sustained recovery.

METHODS: A literature search was performed in databases such as Web of Science, Scopus, and PubMed to identify studies on eSCS for movement disorders.

RESULTS: The therapeutic effects of eSCS are achieved through both immediate facilitative actions and long-term neural reorganization. By activating sensory neurons in the dorsal root, facilitating proprioceptive input and modulating spinal interneurons, eSCS enhances motor neuron excitability. Additionally, eSCS influences corticospinal interactions, increasing cortical excitability and promoting corticospinal circuit remodeling. Neuroplasticity plays a critical role in the long-term efficacy of eSCS, with evidence suggesting that stimulation can enhance axonal sprouting, synaptic formation, and neurotrophic factor expression while reducing neuroinflammation. Its regulation of the sympathetic nervous system further enhances recovery by improving blood flow, muscle tone, and other physiological parameters.

CONCLUSIONS: Epidural spinal cord stimulation shows promise in enhancing motor function and promoting neuroplasticity, but further research is needed to optimize treatment protocols and establish long-term efficacy.},
}

@article {pmid40480249,
year = {2025},
author = {Pritchard, M and Campelo, F and Goldingay, H},
title = {An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ade1f9},
pmid = {40480249},
issn = {1741-2552},
abstract = {Objective: Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG & EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings. Approach: We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection & Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature. Main results: EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems. Significance: To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies, enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.},
}

@article {pmid40479831,
year = {2025},
author = {Li, C and Di, G and Li, Q and Sun, L and Wang, W and Wang, Y and Jiang, X and Wu, J},
title = {Microsurgical anatomy of the fiber tracts and vascular structures lateral to the internal capsule.},
journal = {Journal of neurosurgery},
volume = {},
number = {},
pages = {1-9},
doi = {10.3171/2025.2.JNS243025},
pmid = {40479831},
issn = {1933-0693},
abstract = {OBJECTIVE: The cerebral structures lateral to the internal capsule are frequently involved in studies of nervous system functions and diseases. This study aimed to investigate the fiber tracts and vascular structures of the brain lateral to the internal capsule using cranial specimens and specimen perfusion techniques.

METHODS: Ten cranial specimens were perfused via arteries and veins using specimen perfusion techniques and then processed using the fiber dissection method. The authors studied the fiber tracts and vascular structures from the cerebral cortex to the internal capsule, moving from lateral to medial.

RESULTS: The topographical relationships between the fiber tracts, nuclei, and vascular structures were identified. This was achieved by examining structures from the gray matter cortex of the brain's lateral surface, including U fibers, long association fiber tracts, and the insular lobe, extending to the level of the internal capsule.

CONCLUSIONS: Understanding the anatomical structures of white matter fiber tracts and vascular structures from the brain's lateral surface to the level of the internal capsule aids in planning safe, effective, and minimally invasive surgical procedures. It also contributes to advancements in neuroscience research.},
}

@article {pmid40478867,
year = {2025},
author = {Jiang, M and Luo, Q and Wang, X and Tan, Y},
title = {The "Dogs' Catching Mice" conjecture in Chinese phonogram processing.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0324848},
pmid = {40478867},
issn = {1932-6203},
mesh = {*Phonetics ; Humans ; Male ; *Language ; Semantics ; Female ; China ; Adult ; Animals ; Young Adult ; East Asian People ; },
abstract = {In Chinese phonogram processing studies, it is not strange that phonetic radicals contribute phonologically to phonograms' phonological recognition. The present study, however, based on previous findings of phonetic radicals' proneness to semantic activation, as well as free-standing phonetic radicals' possession of triadic interconnections of orthography, phonology, and semantics at the lexical level, proposed that phonetic radicals may contribute semantically to the host phonograms' phonological recognition. We label this speculation as the "Dogs' Catching Mice" Conjecture. To examine this conjecture, three experiments were conducted. Experiment 1 was designed to confirm whether phonetic radicals, when embedded in phonograms, can contribute semantically to their host phonograms' phonological recognition. Experiment 2 was intended to show that the embedded phonetic radicals employed in Experiment 1 were truly semantically activated. Experiment 3, on top of the first two experiments, was devoted to demonstrating that the semantically activated phonetic radicals, when used as independent characters, can truly contribute semantically to their phonological recognition. Results from the three experiments combine to confirm the conjecture. The implication drawn is that phonetic radicals may have forged two paths in contributing to the host phonograms' phonological recognition: one is the regular "Cats' Catching Mice" Path, the other is the novel "Dogs' Catching Mice" Path.},
}

*Phonetics
Humans
Male
*Language
Semantics
Female
China
Adult
Animals
Young Adult
East Asian People

@article {pmid40478707,
year = {2025},
author = {Li, H and Zhang, H and Chen, Y},
title = {Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3577611},
pmid = {40478707},
issn = {2168-2208},
abstract = {The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characteristics of the multichannel EEG, a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) is proposed in this study. Specifically, by utilizing convolutional neural networks (CNNs) on different branches, the proposed processing network first extracts the temporal-spatial features of the original EEG and the temporal-spectral-spatial features of time-frequency domain data converted by wavelet transformation, respectively. These perceived features are then integrated by a feature fusion block, serving as the input of the transformer to capture the global long-range dependencies entailed in the non-stationary EEG, and being classified via the global average pooling and multi-layer perceptron blocks. To evaluate the efficacy of the proposed approach, the competitive experiments are conducted on three publicly available datasets of BCI IV 2a, BCI IV 2b, and SEED, with the head-to-head comparison of more than ten other state-of-the-art methods. As a result, our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 82.79% in BCI IV 2a, 89.38% in BCI IV 2b, and 96.65% in SEED, respectively. Extensive ablation experiments conducted between the Dual-TSST and comparative baseline model also reveal the enhanced decoding performance with each module of our proposed method. This study provides a new approach to high-performance EEG decoding and has great potential for future CNN-Transformer based applications.},
}

@article {pmid40476694,
year = {2025},
author = {Nazareth, G},
title = {Speaking from the heart: a story about innovation, resilience, and infinite possibilities with AAC.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-2},
doi = {10.1080/07434618.2025.2508491},
pmid = {40476694},
issn = {1477-3848},
abstract = {Communication is the cornerstone of human connection, impacting everything from our personal relationships to our professional success. This concept became heartbreakingly real for me when I was diagnosed with motor neuron disease at the age of 24. The rapid decline of my speech left me feeling all alone and isolated. After experimenting with AAC options, I yearned for a system that was lightweight, portable and stylish. This sparked my entrepreneurial spirit, leading me to assemble components catering to my diverse interests and professional pursuits. Over the years, I have built multiple AAC systems using different hardware platforms. Currently, I am focused on integrating emotional expression and faster communication speeds into AAC technology. Artificial intelligence, multi-modal inputs and non-invasive brain-computer interfaces hold immense potential for people who use AAC. Building my communication tools has revealed profound truths about living life to the fullest, accepting complete responsibility for our lives and embracing the good, the bad and the ugly. Through innovation and resilience, I have discovered infinite possibilities and I continue to use AAC to work miracles in my own life.},
}

@article {pmid40475558,
year = {2025},
author = {Sicorello, M and Gianaros, PJ and Wright, AGC and Bogdan, P and Kraynak, TE and Manuck, SB and Schmahl, C and Wager, TD},
title = {The functional neurobiology of negative affective traits across regions, networks, signatures, and a machine learning multiverse.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.05.15.653674},
pmid = {40475558},
issn = {2692-8205},
abstract = {Understanding the neural basis of negative affective traits like neuroticism remains a critical challenge across psychology, neuroscience, and psychiatry. Here, we investigate which level of brain organization-regions, networks, or validated whole-brain machine-learning signatures-best explains negative affective traits in a community sample of 458 adults performing the two most widely used affective fMRI tasks, viewing emotional faces and scenes. Neuroticism could not be predicted from brain activity, with Bayesian evidence against all theory-guided neural measures. However, preregistered whole-brain models successfully decoded vulnerability to stress, a lower-level facet of neuroticism, with results replicating in a hold-out sample. The neural stress vulnerability pattern demonstrated good psychometric properties and indicated that negative affective traits are best represented by distributed whole-brain patterns related to domain-general stimulation rather than localized activity. Together with results from a comprehensive multiverse analysis across 14 traits and 1,176 models- available for exploration in an online app-the findings speak against simplistic neurobiological theories of negative affective traits, highlight a striking gap between predicting individual differences (r <.35) and within-person emotional states (r =.88), and underscore the importance of aligning psychological constructs with neural measures at the appropriate level of granularity.},
}

@article {pmid40477046,
year = {2020},
author = {Amoo-Adare, EA},
title = {The Art of (Un)Thinking: When Hyper Productivity Says 'Enough!', Is a Feast.},
journal = {Postdigital science and education},
volume = {2},
number = {3},
pages = {606-613},
pmid = {40477046},
issn = {2524-4868},
}

@article {pmid40472937,
year = {2025},
author = {Qian, MB and Huang, JL and Wang, L and Zhou, CH and Zhu, TJ and Zhu, HH and He, YT and Zhou, XN and Lai, YS and Li, SZ},
title = {Clonorchiasis in China: geospatial modelling of the population infected or at risk, based on national surveillance.},
journal = {The Journal of infection},
volume = {},
number = {},
pages = {106528},
doi = {10.1016/j.jinf.2025.106528},
pmid = {40472937},
issn = {1532-2742},
abstract = {OBJECTIVES: Clonorchiasis is highly endemic in China. The unavailability of fine-scale distribution of population with infection and chemotherapy need hinders the control.

METHODS: This study established Bayesian geostatistical models to estimate age- and gender-specific prevalence of Clonorchis sinensis infection at high spatial resolution (5 × 5km[2]), based on the surveillance data in China between 2016 and 2021, together with socioeconomic, environmental and behavioral determinants. The population at risk and under infection, as well as chemotherapy need were then estimated.

RESULTS: In 2020, population-weighted prevalence of 0.67% (95% Bayesian credible interval (BCI): 0.58%-0.77%) was estimated for C. sinensis infection in China, corresponding to 9.46 million (95% BCI: 8.22 million-10.88 million) persons under infection. High prevalence was demonstrated in southern areas including Guangxi (8.92%, 95% BCI: 7.10%-10.81%) and Guangdong (2.99%, 95% BCI: 2.43%-3.74%). A conservative estimation of 99.13 million (95% BCI: 88.61 million-114.40 million) people were at risk of infection, of which 51.69 million (95% BCI: 45.48 million-57.84 million) need chemotherapy.

CONCLUSIONS: Clonorchiasis is an important public health problem in China, especially in southern areas due to the huge population at risk and large number of people under infection. Implementation of chemotherapy is urged to control the morbidity.

Environmental and socioeconomic data are open access (Table S1 in Supplementary Information). Epidemiological and behavioral data are not publicly available but are available on reasonable request after reviewed by the National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research).},
}

@article {pmid40472336,
year = {2025},
author = {Ranieri, A and Pichiorri, F and Colamarino, E and Cincotti, F and Mattia, D and Toppi, J},
title = {SPectral graph theory And Random walK (SPARK) toolbox for static and dynamic characterization of (di)graphs: A tutorial.},
journal = {PloS one},
volume = {20},
number = {6},
pages = {e0319031},
pmid = {40472336},
issn = {1932-6203},
mesh = {Humans ; Algorithms ; Electroencephalography ; Stroke/physiopathology ; *Software ; Brain/physiopathology/physiology ; },
abstract = {Spectral graph theory and its applications constitute an important forward step in modern network theory. Its increasing consensus over the last decades fostered the development of innovative tools, allowing network theory to model a variety of different scenarios while answering questions of increasing complexity. Nevertheless, a comprehensive understanding of spectral graph theory's principles requires a solid technical background which, in many cases, prevents its diffusion through the scientific community. To overcome such an issue, we developed and released an open-source MATLAB toolbox - SPectral graph theory And Random walK (SPARK) toolbox - that combines spectral graph theory and random walk concepts to provide a both static and dynamic characterization of digraphs. Once described the theoretical principles grounding the toolbox, we presented SPARK structure and the list of available indices and measures. SPARK was then tested in a variety of scenarios including: two-toy examples on synthetic networks, an example using public datasets in which SPARK was used as an unsupervised binary classifier and a real data scenario relying on functional brain networks extracted from the EEG data recorded from two stroke patients in resting state condition. Results from both synthetic and real data showed that indices extracted using SPARK toolbox allow to correctly characterize the topology of a bi-compartmental network. Furthermore, they could also be used to find the "optimal" vertex set partition (i.e., the one that minimizes the number of between-cluster links) for the underlying network and compare it to a given a priori partition. Finally, the application to real EEG-based networks provides a practical case study where the SPARK toolbox was used to describe networks' alterations in stroke patients and put them in relation to their motor impairment.},
}

Humans
Algorithms
Electroencephalography
Stroke/physiopathology
*Software
Brain/physiopathology/physiology

@article {pmid40471721,
year = {2025},
author = {Li, J and Fu, B and Li, F and Gu, W and Ji, Y and Li, Y and Liu, T and Shi, G},
title = {Applying SSVEP BCI on Dynamic Background.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3576984},
pmid = {40471721},
issn = {1558-0210},
abstract = {Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high efficiency and accuracy. The SSVEP paradigm and decoding methods have been extensively studied and achieved remarkable results. This study proposed two modulation methods for the SSVEP paradigm, namely color inversion modulation and brightness compression modulation. Color inversion modulation adjusts the stimulus to adapt to the changing background, while brightness compression modulation ensures high contrast by reducing the background brightness. Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. We conduct experiments to evaluate the performance of the proposed modulation and decoding methods. Compared with color inversion modulation and no modulation, the brightness compression modulation method achieved the best performance. In addition, MTSGNN outperforms the best competitive decoding method by 11.98%, 3.9% and 5.15% under color inversion modulation, brightness compression modulation and no modulation, respectively. The experimental results demonstrate the effectiveness of the proposed modulation methods and the robustness of the proposed decoding method. This study significantly improves the performance of SSVEP in dynamic backgrounds and effectively expands the practical application scenarios of BCI.},
}

@article {pmid40471491,
year = {2025},
author = {Liu, X and Jia, Z and Xun, M and Wan, X and Lu, H and Zhou, Y},
title = {MSFHNet: a hybrid deep learning network for multi-scale spatiotemporal feature extraction of spatial cognitive EEG signals in BCI-VR systems.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40471491},
issn = {1741-0444},
support = {62276022//National Natural Science Foundation of China/ ; 62206014//National Natural Science Foundation of China/ ; },
abstract = {The integration of brain-computer interface (BCI) and virtual reality (VR) systems offers transformative potential for spatial cognition training and assessment. By leveraging artificial intelligence (AI) to analyze electroencephalogram (EEG) data, brain activity patterns during spatial tasks can be decoded with high precision. In this context, a hybrid neural network named MSFHNet is proposed, optimized for extracting spatiotemporal features from spatial cognitive EEG signals. The model employs a hierarchical architecture where its temporal module uses multi-scale dilated convolutions to capture dynamic EEG variations, while its spatial module integrates channel-spatial attention mechanisms to model inter-channel dependencies and spatial distributions. Cross-stacked modules further refine discriminative features through deep-level fusion. Evaluations demonstrate the superiority of MSFHNet in the beta2 frequency band, achieving 98.58% classification accuracy and outperforming existing models. This innovation enhances EEG signal representation, advancing AI-powered BCI-VR systems for robust spatial cognitive training.},
}

@article {pmid40470749,
year = {2025},
author = {Li, W and Gao, C and Li, Z and Diao, Y and Li, J and Zhou, J and Zhou, J and Peng, Y and Chen, G and Wu, X and Wu, K},
title = {BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e17408},
doi = {10.1002/advs.202417408},
pmid = {40470749},
issn = {2198-3844},
support = {2023YFC2414500//National Key Research and Development Program of China/ ; 2023YFC2414504//National Key Research and Development Program of China/ ; 81971585//Natural Science Foundation of China/ ; 72174082//Natural Science Foundation of China/ ; 82271953//Natural Science Foundation of China/ ; 82301688//Natural Science Foundation of China/ ; 2021B1515020064//Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project/ ; 2023B0303020001//Key Research and Development Program of Guangdong/ ; 2023B0303010003//Key Research and Development Program of Guangdong/ ; 2022A1515140142//Basic and Applied Basic Research Foundation of Guangdong Province/ ; 2024A1515013058//Natural Science Foundation of Guangdong Province/ ; 202206060005//Science and Technology Program of Guangzhou/ ; 202206080005//Science and Technology Program of Guangzhou/ ; 202206010077//Science and Technology Program of Guangzhou/ ; 202206010034//Science and Technology Program of Guangzhou/ ; 202201010093//Science and Technology Program of Guangzhou/ ; 2023A03J0856//Science and Technology Program of Guangzhou/ ; 2023A03J0839//Science and Technology Program of Guangzhou/ ; },
abstract = {This study presents BrainFusion, a unified software framework designed to improve reproducibility and support translational applications in multimodal brain-computer interface (BCI) and brain-body interaction research. While electroencephalography (EEG) -based BCIs have advanced considerably, integrating multimodal physiological signals remains hindered by analytical complexity, limited standardization, and challenges in real-world deployment. BrainFusion addresses these gaps through standardized data structures, automated preprocessing pipelines, cross-modal feature engineering, and integrated machine learning modules. Its application generator further enables streamlined deployment of workflows as standalone executables. Demonstrated in two case studies, BrainFusion achieves 95.5% accuracy in within-subject EEG-functional near-infrared spectroscopy (fNIRS) motor imagery classification using ensemble modeling and 80.2% accuracy in EEG-electrocardiography (ECG) sleep staging using deep learning, with the latter successfully deployed as an executable tool. Supporting EEG, fNIRS, electromyography (EMG) , and ECG, BrainFusion provides a low-code, visually guided environment, facilitating accessibility and bridging the gap between multimodal research and application in real world.},
}

@article {pmid40469097,
year = {2025},
author = {Wang, Z and Wang, Y},
title = {Multi-branch GAT-GRU-transformer for explainable EEG-based finger motor imagery classification.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1599960},
pmid = {40469097},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) provides a non-invasive and real-time approach to decoding motor imagery (MI) tasks, such as finger movements, offering significant potential for brain-computer interface (BCI) applications. However, due to the complex, noisy, and non-stationary nature of EEG signals, traditional classification methods-such as Common Spatial Pattern (CSP) and Power Spectral Density (PSD)-struggle to extract meaningful, multidimensional features. While deep learning models like CNNs and RNNs have shown promise, they often focus on single-dimensional aspects and lack interpretability, limiting their neuroscientific relevance. This study proposes a novel multi-branch deep learning framework, termed Multi-Branch GAT-GRU-Transformer, to enhance EEG-based MI classification. The model consists of parallel branches to extract spatial, temporal, and frequency features: a Graph Attention Network (GAT) models spatial relationships among EEG channels, a hybrid Gated Recurrent Unit (GRU) and Transformer module captures temporal dependencies, and one-dimensional CNNs extract frequency-specific information. Feature fusion is employed to integrate these heterogeneous representations. To improve interpretability, the model incorporates SHAP (SHapley Additive exPlanations) and Phase Locking Value (PLV) analyses. Notably, PLV is also used to construct the GAT adjacency matrix, embedding biologically-informed spatial priors into the learning process. The proposed model was evaluated on the Kaya dataset, achieving a five-class MI classification accuracy of 55.76%. Ablation studies confirmed the effectiveness of each architectural component. Furthermore, SHAP and PLV analyses identified C3 and C4 as critical EEG channels and highlighted the Beta frequency band as highly relevant, aligning with known motor-related neural activity. The Multi-Branch GAT-GRU-Transformer effectively addresses key challenges in EEG-based MI classification by integrating domain-relevant spatial, temporal, and frequency features, while enhancing model interpretability through biologically grounded mechanisms. This work not only improves classification performance but also provides a transparent framework for neuroscientific investigation, with broad implications for BCI development and cognitive neuroscience research.},
}