@article {pmid40944703,
year = {2025},
author = {Elliss, H and Proctor, K and Robertson, M and Bagnall, J and Kasprzyk-Hordern, B},
title = {A new wide-scope, multi-biomarker wastewater-based epidemiology analytical method to monitor the health and well-being of inhabitants at a metropolitan scale.},
journal = {Analytical and bioanalytical chemistry},
volume = {},
number = {},
pages = {},
pmid = {40944703},
issn = {1618-2650},
abstract = {This manuscript establishes a new, comprehensive biomarker list and a multiresidue trace quantification method for community-wide health and well-being assessment at a metropolitan scale using wastewater-based epidemiology (WBE) and mass spectrometry pipelines. This method enables the quantification of 204 biochemical indicators (BCIs) across a range of biomarker classes within influent wastewater and includes illicit drug BCIs, pharmaceuticals as proxies for disease, health markers (hormones, oxidative stress, lipid peroxidation, etc.), Lifestyle chemicals, food BCIs, and hazardous chemicals in personal care products. This method facilitates the combined assessment of community exposure to chemicals and the effects of this exposure in the same framework. The method enables full quantification of 141 BCIs with method detection Limits varying from 0.01 ng/L for amlodipine to 23.8 ng/L for stachydrine. Total average method accuracies were 102.7% whereas precision was 10.4%. During an initial assessment of this method to test its suitability, 62% of all targets were detected and quantified during a week-long feasibility study of a large city with weekly average Daily BCI loads ranging from 40.0 ± 20.0 mg/day for salbutamol to 5836.5 ± 1697.1 g/day for creatinine. The inclusion of new endogenous markers such as advanced glycation end products, detected in wastewater for the first time, enables more accurate determination of community-level health and lifestyle habits. Alongside an unbiased and comprehensive health assessment through endogenous markers, health is further assessed via the use of pharmaceuticals, acting as a proxy for health and disease status whilst additionally providing insights into community lifestyle habits through the monitoring of licit/illicit drug use and food consumption. The analysis of all biomarker classes combined aims to provide insights to exposure and health effect outcomes at the community level.},
}

@article {pmid40942766,
year = {2025},
author = {Han, Q and Ye, H and Sun, Y and Song, Z and Zhao, J and Shi, L and Kuang, Z},
title = {TopoTempNet: A High-Accuracy and Interpretable Decoding Method for fNIRS-Based Motor Imagery.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175337},
pmid = {40942766},
issn = {1424-8220},
support = {YDZJ202201ZYTS684//Development program project of the Science and Technology Department of Jilin Province, China/ ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) offers a safe and portable signal source for brain-computer interface (BCI) applications, particularly in motor imagery (MI) decoding. However, its low sampling rate and hemodynamic delay pose challenges for temporal modeling and dynamic brain network analysis. To address these limitations in temporal dynamics, static graph modeling, and feature fusion interpretability, we propose TopoTempNet, an innovative topology-enhanced temporal network for biomedical signal decoding. TopoTempNet integrates multi-level graph features with temporal modeling through three key innovations: (1) multi-level topological feature construction using local and global functional connectivity metrics (e.g., connection strength, density, global efficiency); (2) a graph-modulated attention mechanism combining Transformer and Bi-LSTM to dynamically model key connections; and (3) a multimodal fusion strategy uniting raw signals, graph structures, and temporal representations into a high-dimensional discriminative space. Evaluated on three public fNIRS datasets (MA, WG, UFFT), TopoTempNet achieves superior accuracy (up to 90.04% ± 3.53%) and Kappa scores compared to state-of-the-art models. The ROC curves and t-SNE visualizations confirm its excellent feature discrimination and structural clarity. Furthermore, the statistical analysis of graph features reveals the model's ability to capture task-specific functional connectivity patterns, enhancing the interpretability of decoding outcomes. TopoTempNet provides a novel pathway for building interpretable and high-performance BCI systems based on fNIRS.},
}

@article {pmid40942721,
year = {2025},
author = {Kauati-Saito, E and Pereira, ADS and Fontana, AP and de Sá, AMFLM and Soares, JGM and Tierra-Criollo, CJ},
title = {Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {17},
pages = {},
doi = {10.3390/s25175291},
pmid = {40942721},
issn = {1424-8220},
support = {CNPq grants 312592/ 2020-5 and 303066/2025-3//Brazilian institutions National Council for Scientific and Technological Development/ ; CAPES process No. 88887.853338/2023-00 and 23038.008788/2017-27//Coordination of Superior Level Staff Improvement/ ; FINEP process No. 01.24.0122.00//Financier for Studies and Projects/ ; FAPERJ process No. E-26/204.393/2024, 201.618/2025, E-211.635/2021, E-26/202.587/2019, and E-26/ 200.338/2023//the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro/ ; },
abstract = {Stroke is a neurological condition that often results in long-term motor deficits. Given the high prevalence of motor impairments worldwide, there is a critical need to explore innovative neurorehabilitation strategies that aim to enhance the quality of life of patients. One promising approach involves brain-computer interface (BCI) systems controlled by electroencephalographic (EEG) signals elicited when a subject performs motor imagery (MI), which is the mental simulation of movement without actual execution. Such systems have shown potential for facilitating motor recovery by promoting neuroplastic mechanisms. Controlling BCI systems based on MI-EEG signals involves the following sequential stages: recording the raw signal, preprocessing, feature extraction and selection, and classification. Each of these stages can be executed using several techniques and numerous parameter combinations. In this study, we searched for the combination of feature extraction technique, time window, frequency range, and classifier that could provide the best classification accuracy for the BCI Competition 2008 IV 2a benchmark dataset (BCI-C), characterized by EEG-MI data of different limbs (four classes, of which three were used in this work), and the NeuroSCP EEG-MI dataset, a custom experimental protocol developed in our laboratory, consisting of EEG recordings of different movements with the same limb (three classes-right dominant arm). The mean classification accuracy for BCI-C was 76%. When the subjects were evaluated individually, the best-case classification accuracy was 94% and the worst case was 54%. For the NeuroSCP dataset, the average classification result was 53%. The individual subject's evaluation best-case was 71% and the worst case was 35%, which is close to the chance level (33%). These results indicate that techniques commonly applied to classify different limb MI based on EEG features cannot perform well when classifying different MI tasks with the same limb. Therefore, we propose other techniques, such as EEG functional connectivity, as a feature that could be tested in future works to classify different MI tasks of the same limb.},
}

@article {pmid40938318,
year = {2025},
author = {Dash, D and Iwane, F and Hayward, W and Salamanca-Giron, RF and Bönstrup, M and Buch, ER and Cohen, LG},
title = {Sequence action representations contextualize during early skill learning.},
journal = {eLife},
volume = {13},
number = {},
pages = {},
pmid = {40938318},
issn = {2050-084X},
support = {NINDS Intramural Research Program/NS/NINDS NIH HHS/United States ; },
abstract = {Activities of daily living rely on our ability to acquire new motor skills composed of precise action sequences. Here, we asked in humans if the millisecond-level neural representation of an action performed at different contextual sequence locations within a skill differentiates or remains stable during early motor learning. We first optimized machine learning decoders predictive of sequence-embedded finger movements from magnetoencephalographic (MEG) activity. Using this approach, we found that the neural representation of the same action performed in different contextual sequence locations progressively differentiated-primarily during rest intervals of early learning (offline)-correlating with skill gains. In contrast, representational differentiation during practice (online) did not reflect learning. The regions contributing to this representational differentiation evolved with learning, shifting from the contralateral pre- and post-central cortex during early learning (trials 1-11) to increased involvement of the superior and middle frontal cortex once skill performance plateaued (trials 12-36). Thus, the neural substrates supporting finger movements and their representational differentiation during early skill learning differ from those supporting stable performance during the subsequent skill plateau period. Representational contextualization extended to Day 2, exhibiting specificity for the practiced skill sequence. Altogether, our findings indicate that sequence action representations in the human brain contextually differentiate during early skill learning, an issue relevant to brain-computer interface applications in neurorehabilitation.},
}

@article {pmid40937924,
year = {2025},
author = {Qu, Y and Hao, M and Hao, H and Ke, S and Li, Y and Wang, C and Xiao, Y and Jiang, B and Zhou, K and Ding, B and Chu, PK and Yu, XF and Wang, J},
title = {2D Vanadium Carbide/Oxide Heterostructure-Based Artificial Sensory Neuron for Multi-Color Near-Infrared Object Recognition.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e12238},
doi = {10.1002/adma.202512238},
pmid = {40937924},
issn = {1521-4095},
support = {2023YFA0915600//National Key R&D Program of China/ ; 2024A1515030176//Natural Science Foundation of Guangdong Province/ ; 2025B1515020088//Natural Science Foundation of Guangdong Province/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; JCYJ20220818100806014//Shenzhen Science and Technology Program/ ; XDB0930000//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; 52273311//National Natural Science Foundation of China/ ; T2293693//National Natural Science Foundation of China/ ; KCXFZ2024090309420300//Shenzhen Innovation and Entrepreneurship Program-Science and Technology Major Project/ ; GZC20241837//Postdoctoral Fellowship Program of China Postdoctoral Science Foundation/ ; DON-RMG 9229021//City University of Hong Kong Donation Research Grants/ ; 9220061//City University of Hong Kong Donation Research Grants/ ; 2025WK2013//Key Project of Research and Development Plan of Hunan Province/ ; },
abstract = {Near-infrared (NIR) photon detection and object recognition are crucial technologies for all-weather target identification in autonomous navigation, nighttime surveillance, and tactical reconnaissance. However, conventional NIR detection systems, which rely on photodetectors and von Neumann computing algorithms, are plagued by energy inefficiency and signal transmission bottlenecks. Herein, a vanadium carbide/oxide (V2C/V2O5-x) heterostructure is designed and synthesized by a topochemical conversion method. The V2C/V2O5-x heterostructure-based memristor exhibits stable threshold-type resistance switching (RS) behavior with low coefficient of variation in transition voltages (1.62% and 1.7%) over thousands of cycles, and maintains stable performance even after storage for 90 days. Benefiting from the NIR responsivity of V2C and the volatile RS enabled by vacancy-enriched V2O5-x, devices exhibit a linear variation in threshold voltage in response to NIR light power density and wavelength. Based on the multi-color NIR modulable RS characteristics and the YOLOv7 algorithm model, an artificial neural network (ANN) architecture achieves average recognition accuracies of 89.6% for cars and 85.9% for persons on the FLIR dataset. This work reveals a heterostructure with versatile functionalities for neuromorphic devices and establishes a memristor-based ANN platform for multi-color object detection and recognition in complex real-world scenarios.},
}

@article {pmid40936365,
year = {2025},
author = {Liu, Y and Xu, G and Li, C and Ma, Y and Ji, N and Feng, X},
title = {Stretchable Multilevel Mesh Brain Electrodes for Neuroplasticity in Glioma Patients Undergoing Surgery.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e03358},
doi = {10.1002/adhm.202503358},
pmid = {40936365},
issn = {2192-2659},
support = {2023YFB3609002//National Basic Research Program of China/ ; 2023YFB3609002//National Basic Research Program of China/ ; 2022YFC2403905//National Basic Research Program of China/ ; U20A6001//National Natural Science Foundation of China/ ; 11921002//National Natural Science Foundation of China/ ; 12002190//National Natural Science Foundation of China/ ; 2023YFB3609002//National Key R&D Program of China/ ; 2023YFB3609002//National Key R&D Program of China/ ; 2022YFC2403905//National Key R&D Program of China/ ; 2022-2-2047//Capital Health Research and Development of Special Fund/ ; },
abstract = {Brain disease surgical treatment usually leads to neurological dysfunction. Electroencephalogram (EEG)-based neuroplasticity study may facilitate patient nerve function recovery from injury, allowing a return to normal activities. Due to the limitations of wound infections and hair barrier effects, a traditional brain-computer interface system is not applicable to patients after tumor resection. Here, stretchable multilevel mesh brain electrodes with reconfigurable interfaces are developed. The electrode has a multilevel mesh and malleable structure to avoid hair blockage between the electrode and scalp, realizing the conformal attachment of the stretchable multilevel mesh brain electrodes to a nondevelopable curved brain surface. Moreover, the thermally reversible hydrogel forms a good reconfigurable interface contact between the electrode and scalp, reducing postoperative infection and secondary injury risks to ensure the high-quality acquisition EEGs. In this study, a newly invented stretchable multilevel mesh brain electrodes is applied to test the preoperative and postoperative EEGs of recurrent glioblastoma patients for the first time. The obvious inhibitory effects of tumors on brain activity (a-wave signals) are discovered. More importantly, the EEG signals gradually enhance with postoperative recovery, which is mutually confirmed with the Karnofsky score results, showing the possibility of neural function remodeling neurological rehabilitation in adults.},
}

@article {pmid40934551,
year = {2025},
author = {Das, N and Chakraborty, M},
title = {EEGOpt: A performance efficient Bayesian optimization framework for automated EEG signal classification.},
journal = {Computers in biology and medicine},
volume = {197},
number = {Pt B},
pages = {111023},
doi = {10.1016/j.compbiomed.2025.111023},
pmid = {40934551},
issn = {1879-0534},
abstract = {BACKGROUND: Accurate classification of electroencephalography (EEG) signals depends on the optimal combination of signal processing, feature extraction, and classification methods. Since no single approach is suitable across different domains, identifying the best methods for each application remains a critical challenge.

OBJECTIVE: We propose EEGOpt, a Bayesian optimization framework designed to automate and optimize methodological choices in electroencephalography (EEG) signal processing and classification.

METHODS: EEGOpt employed Tree-Structured Parzen Estimator (TPE) to optimize signal denoising, feature extraction, and classifier selection. The search space included Empirical Mode Decomposition and Wavelet Packet Decomposition (WPD) for denoising; spatiotemporal, nonlinear, and spectral features; and classifiers with distinct decision boundaries. A modular caching mechanism was used to minimize redundant computations. EEGOpt was evaluated on three datasets and benchmarked against deep-learning models (EEGNet, ShallowConvNet, and DeepConvNet). TPE was compared with sampling methods, including Gaussian Process, Covariance Matrix Adaptation Evolution Strategy, Quasi-Monte Carlo, and random search.

RESULTS: EEGOpt achieved classification accuracies of up to 99.63 %, outperforming EEGNet (96.20 %), ShallowConvNet (90.83 %), and DeepConvNet (90.29 %). The caching mechanism reduced computation time by 74.69 % compared to no caching, and by 95 % compared to deep learning models. TPE was effective in navigating hierarchical search spaces to locate global optima. EEGOpt identified covariance and wavelet features, k-nearest neighbor classifier, and WPD denoising as optimal for music-based EEG classification.

CONCLUSION: EEGOpt is a scalable and interpretable framework that automatically identifies optimal signal processing and classification strategies adaptable to EEG datasets, making it a valuable tool for neuroscientific research, diagnostics, and brain-computer interface development.},
}

@article {pmid40933818,
year = {2025},
author = {Tang, MY and Zhang, YY and Lin, L and Wu, LL and Hu, MT and Tan, LH and Yu, CX and Wang, H and Yu, YQ and Ding, Y and Han, JX and Hu, H and Li, XM and Lian, H},
title = {Medial preoptic CCKAR mediates anxiety and aggression induced by chronic emotional stress in male mice.},
journal = {National science review},
volume = {12},
number = {10},
pages = {nwaf152},
pmid = {40933818},
issn = {2053-714X},
abstract = {Anxiety disorders frequently accompany aggression, with their co-occurrence predicting greater functional impairment and poor prognosis. Nevertheless, the underlying neural mechanisms remain elusive, primarily due to a lack of appropriate animal models. Here, we designed a chronic conspecific outsider stress (CCS) model in which male mice underwent perceived social threats and exhibited increased anxiety-like behaviors accompanied by aggression. CCS led to Fos activation and hyperexcitability of GABAergic neurons in the medial preoptic area (mPOA). Inhibition of mPOA GABAergic (mPOA[Gad2]) neurons rescued CCS-induced anxiety-like and aggressive behaviors, whereas activating these cells induced susceptibility to CCS. Moreover, CCS upregulated the mRNA and protein expression of the sexual-dimorphic gene, cholecystokinin A receptor (CCKAR)-encoding Cckar gene in the mPOA. Importantly, the knock-down and overexpression of CCKAR in the mPOA[Gad2] neurons had alleviating and promoting effects on anxiety-like and aggressive behaviors, aligning with decreased and increased excitability by the anxiolytic CCKAR antagonist MK-329 and the anxiogenic CCKAR agonist A71623 in mPOA[Gad2] neurons, respectively. Overall, our study characterizes a novel mouse model of anxiety disorders accompanied by aggression and the neuronal subpopulation and molecular mediator of the aberrant behaviors provide potential targets of intervention for anxiety disorders with aggression.},
}

@article {pmid40933619,
year = {2025},
author = {Hu, D and Li, H and Takahata, T and Tanigawa, H},
title = {Clustered architecture of ipsilateral and interhemispheric connections in macaque ventrolateral prefrontal cortex.},
journal = {Frontiers in neural circuits},
volume = {19},
number = {},
pages = {1635105},
doi = {10.3389/fncir.2025.1635105},
pmid = {40933619},
issn = {1662-5110},
abstract = {The fine-scale organization of intrinsic and extrinsic connections in the primate ventrolateral prefrontal cortex (VLPFC), a region essential for higher cognitive functions, remains poorly understood. This contrasts with, for example, the well-documented stripe-like intrinsic circuits of the dorsolateral prefrontal cortex (DLPFC). To elucidate the circuit architecture supporting VLPFC function, we investigated the spatial organization of connections targeting the caudal VLPFC (primarily area 45A) in macaque monkeys using multiple retrograde tracers. Analyzing the distribution of labeled neurons in flattened tangential sections revealed that laterally projecting connections within the same hemisphere formed distinct clusters, not only in the VLPFC but also in the DLPFC. These clusters often spanned multiple cortical layers, suggesting a columnar-like organization. The width (minor axis) of these clusters was approximately 1.2 mm. Similarly, contralateral callosal projection neurons were also arranged in clusters. Additionally, inputs originating from the superior temporal sulcus were found to arise from discrete clusters of neurons. Our findings demonstrate that both long-range ipsilateral and interhemispheric connections of the caudal VLPFC share a common, fine-scale clustered architecture. This study provides an anatomical framework for understanding the structural basis of information processing and interhemispheric coordination within this critical association cortex, suggesting that this architecture is fundamental to VLPFC's role in complex cognitive functions.},
}

@article {pmid40932879,
year = {2025},
author = {Attar, ET},
title = {EEG-based characterization of auditory attention and meditation: an ERP and machine learning approach.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1616456},
doi = {10.3389/fnhum.2025.1616456},
pmid = {40932879},
issn = {1662-5161},
abstract = {INTRODUCTION: This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study evaluated event-related potentials alongside theta, alpha and beta spectral power while employing machine learning techniques to distinguish meditative states from cognitive tasks.

METHODS: The study utilized data from 13 participants aged 24-58, which researchers obtained through an openly accessible OpenNeuro dataset.

RESULT: Examination of eventrelated potentials (ERPs) demonstrated that P300 amplitude showed significant growth when responding to oddball stimuli, which indicates increased attention allocation (p < 0.05). Spectral power analysis demonstrated an increase in frontal alpha and beta power during meditation while central theta power decreased, which suggests reduced cognitive load and enhanced internal focus. Meditation experience showed a statistical relationship with frontal alpha power, where r = 0.45 and p < 0.03. A Random Forest classifier reached 86. The system achieved a 7% accuracy rate in differentiating cognitive from meditative states while identifying P300 amplitude and frontal alpha power, together with beta power as significant predictors.

CONCLUSION: The EEG-based neurofeedback systems demonstrate potential alongside real-time cognitive state detection for healthcare brain-computer interfaces and mental health applications. The study of meditation's effects on brain activity reveals its benefits for emotional regulation and concentration improvement. The research findings deliver strong evidence that meditation induces distinct neural modifications detectable through ERP and spectral analysis. The potential for meditation to enhance cortical efficiency alongside emotion self-regulation indicates its viability as a mental health support tool. The integration of EEG biomarkers with machine learning methods emerges as a potential pathway for real-time cognitive and emotional state monitoring which enables tailored interventions through neurofeedback systems and brain-computer interfaces to boost cognitive function and emotional health across clinical settings and everyday life.},
}

@article {pmid40931087,
year = {2025},
author = {Liu, W and Xiang, M and Ding, N},
title = {Active use of latent tree-structured sentence representation in humans and large language models.},
journal = {Nature human behaviour},
volume = {},
number = {},
pages = {},
pmid = {40931087},
issn = {2397-3374},
abstract = {Understanding how sentences are represented in the human brain, as well as in large language models (LLMs), poses a substantial challenge for cognitive science. Here we develop a one-shot learning task to investigate whether humans and LLMs encode tree-structured constituents within sentences. Participants (total N = 372, native Chinese or English speakers, and bilingual in Chinese and English) and LLMs (for example, ChatGPT) were asked to infer which words should be deleted from a sentence. Both groups tend to delete constituents, instead of non-constituent word strings, following rules specific to Chinese and English, respectively. The results cannot be explained by models that rely only on word properties and word positions. Crucially, based on word strings deleted by either humans or LLMs, the underlying constituency tree structure can be successfully reconstructed. Altogether, these results demonstrate that latent tree-structured sentence representations emerge in both humans and LLMs.},
}

@article {pmid40930287,
year = {2025},
author = {Shirodkar, VR and Edla, DR and Kumari, A and Dharavath, R},
title = {Deep feature extraction and swarm-optimized enhanced extreme learning machine for motor imagery recognition in stroke patients.},
journal = {Journal of neuroscience methods},
volume = {424},
number = {},
pages = {110565},
doi = {10.1016/j.jneumeth.2025.110565},
pmid = {40930287},
issn = {1872-678X},
abstract = {BACKGROUND: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.

NEW METHODS: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification. The approach begins with subject-specific frequency band selection based on event-related desynchronization (ERD), aimed at reducing non-stationarity and improving signal relevance. Spatial and temporal features are then extracted using a combination of the scale-invariant feature transform (SIFT) and a one-dimensional convolutional neural network (1D CNN), providing a comprehensive representation of EEG signal dynamics. These features are fused and classified using an enhanced extreme learning machine (EELM), with hidden layer weights optimized using differential evolution (DE), particle swarm optimization (PSO), and dynamic multi-swarm PSO (DMS-PSO).

RESULTS: Experimental validation on a dataset of 50 stroke patients demonstrated an average classification accuracy of 97% using DMS-PSO with 10-fold cross-validation. Additional evaluation on the BCI Competition IV 1a dataset yielded 95% and 91.56% on IV 2a, indicating strong generalization performance.

Unlike conventional BCI approaches, this method combines adaptive filtering, spatial-temporal hybrid feature learning, and metaheuristic optimization, resulting in a lightweight model with improved classification accuracy and robustness.

CONCLUSION: These findings demonstrate the effectiveness of evolutionary optimization in dealing with the constraints provided by high-dimensional, non-stationary EEG data, making it a promising strategy for real-time MI classification in BCI-based stroke applications.},
}

@article {pmid40929275,
year = {2025},
author = {Kim, TY and Son, Y and Yook, KY and Lee, DG and Kim, Y and Kim, SJ and Park, K and Lee, Y and Lee, TK and Chung, JJ and Yang, C and Park, S and Seo, J},
title = {Bioadaptive liquid-infused multifunctional fibers for long-term neural recording via BDNF stabilization and enhanced neural interaction.},
journal = {Science advances},
volume = {11},
number = {37},
pages = {eadz1228},
doi = {10.1126/sciadv.adz1228},
pmid = {40929275},
issn = {2375-2548},
mesh = {*Brain-Derived Neurotrophic Factor/chemistry/metabolism ; *Neurons/physiology/metabolism/drug effects ; Animals ; *Brain-Computer Interfaces ; Astrocytes/metabolism ; Coated Materials, Biocompatible/chemistry ; Rats ; },
abstract = {Brain-computer interfaces (BCIs) enable direct communication between the brain and computers. However, their long-term functionality remains limited due to signal degradation caused by acute insertion trauma, chronic foreign body reaction (FBR), and biofouling at the device-tissue interface. To address these challenges, we introduce a multifunctional surface modification strategy called targeting-specific interaction and blocking nonspecific adhesion (TAB) coating for flexible fiber, achieving a synergistic integration of mechanical compliance and biochemical stability. The coating combines brain-derived neurotrophic factor (BDNF) conjugation and a lubricant-infused surface. This dual-functional design enables selective interaction with neurons and astrocytes while preventing nonspecific adhesion. Notably, high-quality single-unit neural signals were stably recorded for more than 12 months after implantation, demonstrating exceptional long-term recording performance. Integrating mechanical compatibility, antifouling properties, and selective neural cell interaction, the TAB-coated multifunctional fiber represents a transformative approach for neural implants, bridging biological systems with computational systems.},
}

*Brain-Derived Neurotrophic Factor/chemistry/metabolism
*Neurons/physiology/metabolism/drug effects
Animals
*Brain-Computer Interfaces
Astrocytes/metabolism
Coated Materials, Biocompatible/chemistry
Rats

@article {pmid40928672,
year = {2025},
author = {Mou, F and Lv, Z and Jin, X and Pan, J and Yun, L and Chen, Z},
title = {Decoding binocular color differences via EEG signals: linking ERP dynamics to chromatic disparity in CIELAB space.},
journal = {Experimental brain research},
volume = {243},
number = {10},
pages = {209},
pmid = {40928672},
issn = {1432-1106},
support = {62165019//National Science Foundation of China/ ; 202305 AC160084//Yunnan Youth and Middle-aged Academic and Technical Leaders Reserve Talent Program/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Male ; Adult ; Female ; *Color Perception/physiology ; Young Adult ; *Evoked Potentials, Visual/physiology ; Photic Stimulation ; *Vision, Binocular/physiology ; *Vision Disparity/physiology ; Neural Networks, Computer ; Event-Related Potentials, P300/physiology ; *Evoked Potentials/physiology ; Support Vector Machine ; },
abstract = {This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences. Four classification models-Support Vector Machines (SVM), EEGNet, Temporal Convolutional Neural Network (T-CNN), and a hybrid CNN-LSTM model were employed to decode EEG data. The highest accuracy reached was 81.93% for binary classification tasks (the largest color differences) and 54.47% for a more nuanced four-class categorization, significantly exceeding random chance. This research offers the first evidence that binocular color differences can be objectively decoded through EEG signals, providing insights into the neural mechanisms of visual perception and forming a basis for developing color-based brain-computer interfaces (BCIs).},
}

Humans
*Electroencephalography/methods
Male
Adult
Female
*Color Perception/physiology
Young Adult
*Evoked Potentials, Visual/physiology
Photic Stimulation
*Vision, Binocular/physiology
*Vision Disparity/physiology
Neural Networks, Computer
Event-Related Potentials, P300/physiology
*Evoked Potentials/physiology
Support Vector Machine

@article {pmid40926818,
year = {2025},
author = {Wu, S and Li, K and Long, J and Zhang, C and Li, R and Cheng, B and Cao, M and Deng, W},
title = {Risk Factors for Postictal Delirium in Geriatric Patients Undergoing Electroconvulsive Therapy: The Role of Lithium and Quetiapine.},
journal = {Alpha psychiatry},
volume = {26},
number = {4},
pages = {45431},
pmid = {40926818},
issn = {2757-8038},
abstract = {BACKGROUND: Postictal delirium (PID) is a significant and often underrecognized adverse effect associated with electroconvulsive therapy (ECT) in geriatric patients. Despite its clinical relevance, the specific risk factors contributing to the development of PID in this vulnerable population remain inadequately understood, which may affect treatment outcomes and patient safety.

METHODS: In this retrospective study, we analyzed data from 168 elderly patients who underwent ECT between 2009 and 2020 at a general hospital in China. Univariate analyses of sociodemographic and clinical characteristics were performed to identify variables for inclusion in a logistic regression model. Multiple binary logistic regression analysis was performed to determine the relationship between these variables and PID occurrence.

RESULTS: The incidence of PID was 20.8% (35/168) among the study cohort. Univariate analysis revealed statistically significant differences between PID and non-PID groups for lithium (χ [2] = 6.67, p = 0.010), quetiapine (χ [2] = 4.36, p = 0.037), number of ECT sessions (U = 3065.50, p = 0.003), and response rate (χ [2] = 12.86, p < 0.001). Logistic regression analysis demonstrated that lithium (odds ratio (OR) = 5.128; p = 0.009) and quetiapine (OR = 2.562; p = 0.024) were significantly associated with PID.

CONCLUSION: Our findings indicate that lithium and quetiapine use significantly increase the risk of developing PID, underscoring the need for clinical vigilance. Careful consideration of these medications when planning ECT treatment is recommended to minimize the risk of postictal complications and optimize therapeutic outcomes.},
}

@article {pmid40925395,
year = {2025},
author = {Bisla, M and Anand, RS},
title = {Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae04ee},
pmid = {40925395},
issn = {2057-1976},
abstract = {Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to communicate through neural signals. The prime objective of this work is to propose an innovative machine learning (ML) based classification methodology that combines electroencephalogram (EEG) data augmentation using a sliding window technique with statistical feature extraction from the amplitude and phase spectrum of frequency domain EEG segments. This work uses an EEG dataset recorded from a 64 channel device during the imagination of long-words, short words, and vowels with 15 human subjects. First, the raw EEG data is filtered between 1 Hz and 100 Hz, then segmented using a sliding window-based data augmentation technique with a window size of 100 and 50% overlap. The Fourier Transform is applied to each windowed segment to compute the amplitude and phase spectrum of the signal at each frequency point. The next step is to extract 50 statistical features from the amplitude and phase spectrum of frequency domain segments. Out of these, the 25 most statistically significant features are selected by applying the Kruskal-Wallis test.The extracted feature vectors are classified using six different machine learning based classifiers named support vector machine (SVM), K nearest neighbor (KNN), Random Forest (RF), XGBoost, LightGBM, and CatBoost. The CatBoost classifier outperforms other machine learning classifiers by achieving the highest accuracy of 91.72±1.52% for long words classification, 91.68±1.54% for long vs short word classification, 88.05±3.07% for short word classification, and 88.89±1.97% for vowel classification. The performance of the proposed model is assessed using five performance evaluation metrics: accuracy, F1-score, precision, recall, and Cohen's kappa. Compared to the existing literature, this study has achieved a 5%-7 % improvement with the CatBoost classifier and extracted feature matrix. .},
}

@article {pmid40922973,
year = {2025},
author = {Denis-Robichaud, J and Nicola, I and Chupin, H and Roy, JP and Buczinski, S and Fauteux, V and Picard-Hagen, N and Dubuc, J},
title = {Nonesterified fatty acids during the dry period and their association with peripartum disorders, culling, and pregnancy in dairy cows.},
journal = {JDS communications},
volume = {6},
number = {5},
pages = {688-693},
pmid = {40922973},
issn = {2666-9102},
abstract = {The objective of this ambidirectional observational cohort study was to explore how nonesterified fatty acids (NEFA) 22 to 35 d before calving were related to NEFA 1 to 14 d before calving and to determine a threshold that could be used to identify cows at risk of poor postpartum health. We enrolled 855 dairy cows from 46 herds, 362 prospectively and 493 retrospectively. The NEFA concentrations were measured during the far-off period (foNEFA; 3 to 5 wk before calving) and in the close-up period (cuNEFA; up to 2 wk before calving), and postpartum infectious and metabolic disorders, reproduction success, and culling were recorded. Using a split dataset, we (1) determined a threshold maximizing the sum of sensitivity and specificity to identify peripartum conditions by classifying elevated NEFA and (2) assessed the associations between elevated NEFA and altered health and reproduction. The associations were expressed as the odds ratio (OR) and the 95% Bayesian credible interval (BCI). The concentration of foNEFA varied from 60 to 700 µmol/L (median = 149), and a threshold of ≥160 µmol/L was identified. Cows with elevated foNEFA had greater odds to have elevated cuNEFA (OR = 183, 95% BCI = 52.1-458), hyperketonemia (OR = 2.0, 95% BCI = 1.0-3.6), displaced abomasum (OR = 12.3, 95% BCI = 1.6-45.8), metritis (OR = 9.4, 95% BCI = 1.3-36.0), and clinical mastitis (OR = 5.8, 95% BCI = 1.9-12.1) than cows below the threshold. Our results suggest that foNEFA, using a threshold of ≥160 µmol/L, could be used by veterinarians as a monitoring or investigating tool to assess cows' negative energy balance before calving, even earlier than 2 wk prepartum. This monitoring could be used to implement early corrective actions to prevent the effect of negative energy balance on reproduction and peripartum health.},
}

@article {pmid40633885,
year = {2025},
author = {Li, J and Chen, H and Liao, W},
title = {Biologically Annotated Heterogeneity of Depression Through Neuroimaging Normative Modeling.},
journal = {Biological psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.biopsych.2025.07.002},
pmid = {40633885},
issn = {1873-2402},
abstract = {Depression is not a unitary disorder; it is heterogeneous in nature. Likewise, no 2 individuals with depression are entirely alike, and therefore their associated symptoms are highly personalized. Over the past decade, numerous approaches have been developed to identify neuroimaging-derived biomarkers to advance our understanding of the neurobiology of patients with depression at the group level. However, substantial clinical heterogeneity among individuals with depression hinders the development of biomarkers for personalized interventions. Recently, publicly available resources have enabled researchers to investigate precision neuromarkers for depression using integrative multineuroimaging approaches. In this review, we systematically revisit previous findings and discuss the advances in data-driven neuroimaging analyses for depression heterogeneity, including the disentangling of dimensional and overlapping strategies, individual-specific abnormal patterns based on normative modeling frameworks, and associations between multiscale organizations. We also discuss the limitations, challenges, and future directions for depression heterogeneity. A summary of these advances is crucial for enhancing the understanding of the neurobiology of depression and will facilitate more accurate diagnoses and personalized interventions.},
}

@article {pmid40922815,
year = {2025},
author = {Zhang, Y and Zhou, M and Liang, R and Chen, J and Shi, P and Zheng, Y and Luo, X and Wu, Y and Yu, X and Wu, Y and Liang, S and Deng, W and Bueber, MA and Phillips, MR and Li, T},
title = {Mental health literacy and the stigmatisation and discrimination of individuals affected by mental illnesses in China: a scoping review.},
journal = {The Lancet regional health. Western Pacific},
volume = {61},
number = {},
pages = {101642},
pmid = {40922815},
issn = {2666-6065},
abstract = {Low mental health literacy (MHL) could contribute to misconceptions about mental illnesses and reinforce various forms of stigma (public, personal, and associative), leading to discrimination, reduced help-seeking, and poorer mental health outcomes. To summarise the current state of the literature on MHL, stigma, and discrimination, this scoping review identified 387 studies published from 2000 to 2024 in five English and three Chinese databases: 60.7% focused on stigma, 31.8% on MHL, and only 7.5% on discrimination. Most studies (84.8%) were descriptive cross-sectional studies, 14.5% evaluated interventions, and 0.7% were non-intervention longitudinal studies. Methodological quality was generally low: reports about 88.4% of the cross-sectional studies, 75.6% of the randomised controlled trials, and 83.4% of the quasi-experimental studies lacked descriptions of key methodological or statistical details. After excluding researcher-developed tools only reported in a single study, 125 assessment tools remained, 26.4% of which were developed in China. Although 21 different mental health conditions were studied, 91.0% of the studies focused on a single condition. Study locations were geographically skewed (one-third of all studies were conducted in Guangdong, Beijing, and Shanghai), and study participants were not representative of the target cohort. The number of publications increased substantially after 2010. Most of the 56 intervention studies, which primarily used psychoeducational interventions, reported improved MHL and decreased stigma. Recommendations for future studies include: 1) Develop standardised instruments to improve comparability. 2) Ensure detailed statistical analyses and clearly defined sample characteristics. 3) Assess variations in MHL, stigmatisation, and discrimination across different mental health conditions. 4) Increase research in underserved regions and conduct nationwide longitudinal studies. 5) Include a broader range of participants in intervention studies and consider new intervention strategies (i.e., other than psychoeducation interventions). 6) Align research objectives with national mental health policies to enhance their relevance and impact.},
}

@article {pmid40921216,
year = {2025},
author = {Saeed, S and Wang, H and Kong, L and Geng, Y and Zhang, J and Pan, Y and Le, X and Zhang, X and Liu, TT and Hu, S},
title = {Machine learning-enhanced mapping of suicide risk in bipolar disorder: A multi-modal analysis.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {120183},
doi = {10.1016/j.jad.2025.120183},
pmid = {40921216},
issn = {1573-2517},
abstract = {BACKGROUND: Bipolar disorder (BD) is associated with a high risk of suicide, but the complex interplay of factors contributing to this risk remains poorly understood. This study aimed to comprehensively analyze demographic, clinical, and biological factors associated with suicide risk in BD patients and develop a novel suicide risk assessment model integrating these factors.

METHODS: We conducted a cross-sectional study of 152 patients with BD, classified into four suicide risk groups: no risk (n = 19), low risk (n = 45), moderate risk (n = 38), and high risk (n = 50). Participants underwent assessments using the Mini-International Neuropsychiatric Interview (M.I.N·I.), Hamilton Depression Rating Scale-24 items (HAMD-24), Young Mania Rating Scale (YMRS), Montgomery-Åsberg Depression Rating Scale (MADRS), and Beck Scale for Suicide Ideation (BSSI). We evaluated thyroid function, inflammatory markers, and lymphocyte subsets. Univariate and multivariate analyses were performed to identify factors associated with suicide risk.

FINDINGS: Depressive symptoms were significantly associated with increased odds of medium (odds ratio (OR) = 1.452, 95 % confidence interval (CI): 1.122-1.878, P = 0.005) and high (OR = 1.405, 95 % CI: 1.091-1.810, P = 0.009) suicide risk. Lower free thyroxine 4 (FT4) levels were associated with higher odds of low (OR = 0.581, 95 % CI: 0.404-0.835, P = 0.003) and medium (OR = 0.694, 95 % CI: 0.486-0.992, P = 0.045) risk. The no-risk group exhibited higher levels of thyroid hormones and autoantibodies. CD3+ T cell percentages varied significantly across risk groups, with the lowest mean percentage in the no-risk group (57.59 ± 14.64 %). Our machine learning models achieved 87.1 % accuracy in predicting suicide risk. Patient Health Questionnaire-9 items, Hamilton Depression Rating Scale-24 items, and Montgomery-Åsberg Depression Rating Scale scores were identified as the strongest predictors of suicide risk through 100 decision trees of Random Forest. In addition, FT4 and interferon-γ emerged as notable contributors to the model's predictions.

CONCLUSION: Depressive symptoms and thyroid function are crucial factors in assessing suicide risk in BD. Thyroid autoimmunity and T cell-mediated immunity emerge as potential biomarkers for risk stratification and therapeutic targets, offering new avenues for personalized intervention strategies.},
}

@article {pmid40920866,
year = {2025},
author = {Nyawanda, BO and Sullivan, KM and Tinkitina, B and Beinamaryo, P and Nabatte, B and Kyarisiima, H and Mubangizi, A and Emerson, PM and Utzinger, J and Vounatsou, P},
title = {Geostatistical analysis to guide treatment decisions for soil-transmitted helminthiasis control in Uganda.},
journal = {PLoS neglected tropical diseases},
volume = {19},
number = {9},
pages = {e0013467},
doi = {10.1371/journal.pntd.0013467},
pmid = {40920866},
issn = {1935-2735},
abstract = {BACKGROUND: Soil-transmitted helminth (STH) infections remain a public health problem in Uganda despite biannual national deworming campaigns implemented since the early 2000s. Recent surveys have indicated a heterogeneous STH infection prevalence, suggesting that the current blanket deworming strategy may no longer be cost-effective. This study identified infection predictors, estimated the geographic distribution of STH infection prevalence by species, and calculated deworming needs for school-age children (SAC).

METHODOLOGY: Bayesian geostatistical models were applied to STH survey data (2021-2023) for each species (i.e., Ascaris lumbricoides, hookworm, and Trichuris trichiura). Climatic, environmental, and socioeconomic predictors were obtained from remote sensing sources, model-based databases, and demographic and health surveys. Prevalence was predicted on a 1 × 1 km2 grid across Uganda, and district-level estimates were used to classify each district into treatment frequency categories and to determine its deworming tablet requirements.

PRINCIPAL FINDINGS: The national prevalence of A. lumbricoides, T. trichiura, and hookworm was estimated at 5.0% (95% Bayesian credible interval [BCI]: 0.8-11.8%), 3.5% (0.7-9.3%), and 7.2% (0.55.7-32.911.1%), respectively. The overall prevalence of any STH infection was 14.3% (9.6-21.8%). High intra-district variation in prevalence was observed. Of 146 implementation units (136 districts and 10 cities), 49 require twice-year treatment, 34 once-yearly treatment, 61 every other year treatment, and 2 had a prevalence <2%, indicating treatment suspension or event-based treatment. Approximately 17million tablets will be needed for preventive chemotherapy aimed at SAC in 2025.

CONCLUSIONS/SIGNIFICANCE: The prevalence of STH infection has declined considerably across Uganda compared to the early 2000s. However, deworming needs remain heterogeneous across districts. Through geostatistical modeling, districts were classified according to the latest World Health Organization's (WHO) treatment guidelines. This approach optimizes treatment distribution and allows for prioritization of populations with the greatest needs. We estimated that tablet requirements are approximately 40% lower compared to the current twice-a-year deworming regimen, which contributes towards WHO's goal of halving the number of tablets required for PC by 2030.},
}

@article {pmid40919632,
year = {2025},
author = {Zhang, S and Ling, C and Wu, J and Li, J and Wang, J and Yu, Y and Liu, X and Lv, J and Vai, MI and Chen, R},
title = {EEG-ERnet: Emotion Recognition based on Rhythmic EEG Convolutional Neural Network Model.},
journal = {Journal of integrative neuroscience},
volume = {24},
number = {8},
pages = {41547},
doi = {10.31083/JIN41547},
pmid = {40919632},
issn = {0219-6352},
support = {2024B03J1361//Guangzhou Science and Technology Plan Project/ ; 2023B03J1327//Guangzhou Science and Technology Plan Project/ ; 2024SZFZ007//Research Fund of Key Laboratory of Numerical Simulation of Sichuan Provincial Universities/ ; 2025ZNSFSC0780//Sichuan Science and Technology Program/ ; 23XXK0402//Foundation of the 2023 Higher Education Science Research Plan of the China Association of Higher Education/ ; CSXL-25102//Foundation of the Sichuan Research Center of Applied Psychology (Chengdu Medical College)/ ; NJ2024ZD014//Neijiang Philosophy and Social Science Planning Project/ ; 2023KQNCX036//Guangdong Province Ordinary Colleges and Universities Young Innovative Talents Project/ ; 22GPNUZDJS17//Scientific Research Capacity Improvement Project of the Doctoral Program Construction Unit of Guangdong Polytechnic Normal University/ ; 2023YJSY04002//Graduate Education Demonstration Base Project of Guangdong Polytechnic Normal University/ ; 2025-M10//Open Research Fund of State Key Laboratory of Digital Medical Engineering/ ; 2022SDKYA015//Research Fund of Guangdong Polytechnic Normal University/ ; },
mesh = {Humans ; *Emotions/physiology ; *Electroencephalography/methods ; *Neural Networks, Computer ; Adult ; Young Adult ; *Recognition, Psychology/physiology ; *Evoked Potentials/physiology ; Brain-Computer Interfaces ; Male ; *Brain Waves/physiology ; Female ; Convolutional Neural Networks ; },
abstract = {BACKGROUND: Emotion recognition from electroencephalography (EEG) can play a pivotal role in the advancement of brain-computer interfaces (BCIs). Recent developments in deep learning, particularly convolutional neural networks (CNNs) and hybrid models, have significantly enhanced interest in this field. However, standard convolutional layers often conflate characteristics across various brain rhythms, complicating the identification of distinctive features vital for emotion recognition. Furthermore, emotions are inherently dynamic, and neglecting their temporal variability can lead to redundant or noisy data, thus reducing recognition performance. Complicating matters further, individuals may exhibit varied emotional responses to identical stimuli due to differences in experience, culture, and background, emphasizing the necessity for subject-independent classification models.

METHODS: To address these challenges, we propose a novel network model based on depthwise parallel CNNs. Power spectral densities (PSDs) from various rhythms are extracted and projected as 2D images to comprehensively encode channel, rhythm, and temporal properties. These rhythmic image representations are then processed by a newly designed network, EEG-ERnet (Emotion Recognition Network), developed to process the rhythmic images for emotion recognition.

RESULTS: Experiments conducted on the dataset for emotion analysis using physiological signals (DEAP) using 10-fold cross-validation demonstrate that emotion-specific rhythms within 5-second time intervals can effectively support emotion classification. The model achieves average classification accuracies of 93.27 ± 3.05%, 92.16 ± 2.73%, 90.56 ± 4.44%, and 86.68 ± 5.66% for valence, arousal, dominance, and liking, respectively.

CONCLUSIONS: These findings provide valuable insights into the rhythmic characteristics of emotional EEG signals. Furthermore, the EEG-ERnet model offers a promising pathway for the development of efficient, subject-independent, and portable emotion-aware systems for real-world applications.},
}

Humans
*Emotions/physiology
*Electroencephalography/methods
*Neural Networks, Computer
Adult
Young Adult
*Recognition, Psychology/physiology
*Evoked Potentials/physiology
Brain-Computer Interfaces
Male
*Brain Waves/physiology
Female
Convolutional Neural Networks

@article {pmid40919177,
year = {2025},
author = {Sha, G and Liu, Y and Cao, Y and Zhang, Q and Zhang, Y and Chen, Y and Fan, Q and Cheng, Y},
title = {Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy.},
journal = {Magnetic resonance letters},
volume = {5},
number = {2},
pages = {200156},
pmid = {40919177},
issn = {2772-5162},
abstract = {Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N = 51), patients with minimal hepatic encephalopathy (MHE, N = 50), patients with overt hepatic encephalopathy (OHE, N = 51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p < 0.001, p = 0.009) as compared to the HC and MHE group respectively, which is further corroborated by the significant correlation between the cortical thickness and digit symbol test (DST) scores. Furthermore, the decreased FC between the right postcentral gyrus and several sensory regions (bilateral somatosensory and visual cortices) was found to be significant in MHE patients as compared to the HC group. Our results revealed cross-sectional structural and functional variations concerning disease progression across different subsystems (e.g., visual, motor and sensory), providing evidence that can potentially explain the mechanisms underlying the sensorimotor and cognitive deficits related to HE.},
}

@article {pmid40916208,
year = {2025},
author = {Ahmadi Seyedkhani, S and Iraji Zad, A and Mohammadpour, R and Taghipoor, M and Vafaiee, M},
title = {Novel Brain-Inspired Hierarchical Micro-Nanostructured Poly(3,4-ethylenedioxythiophene)/Polydopamine Neural Interface on Titanium Nitride Electrodes for Electrophysiological Signal Recording.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.5c01451},
pmid = {40916208},
issn = {2576-6422},
abstract = {The development of high-performance neural interfaces is critical for advancing brain-machine communication and treating neurological disorders. A major challenge in neural electrode design is achieving a seamless biological-electronic interface with optimized electrochemical properties, mechanical stability, and biocompatibility. In this study, we introduce a hierarchical micronanostructured poly(3,4-ethylenedioxythiophene)-polydopamine (PEDOT-PDA) coating on titanium nitride (TiN) microelectrodes engineered to enhance electrophysiological signal recording and neural integration. The PEDOT-PDA films were synthesized via potentiodynamic electropolymerization, achieving a 90% reduction in impedance (∼353 Ω at 1 kHz) compared to conventional gold (Au) electrodes (∼3795 Ω) and a 60% decrease relative to TiN substrates (∼890 Ω). The brain-inspired hierarchical micronanostructure mimics the extracellular matrix (ECM), improving cell adhesion and biointegration. Wettability analysis revealed a 63% enhancement in hydrophilicity, reducing the water contact angle from ∼70° for pure PEDOT to ∼25° for PEDOT-PDA. Biocompatibility assessments demonstrated excellent cell viability of ∼97% for PEDOT-PDA electrodes and superior cell attachment with extended filopodia formation, promoting long-term neural interface stability. The PEDOT-PDA interface outperforms conventional PEDOT and metal-based electrodes in electrochemical stability, biocompatibility, and signal recording efficiency, making it a promising candidate for next-generation brain-computer interfaces (BCIs).},
}

@article {pmid40915552,
year = {2025},
author = {Wang, X and Zou, T and Wang, H and Han, H and Chen, H and Calhoun, VD and Li, R},
title = {A dynamic spatiotemporal representation framework for deciphering personal brain function.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121443},
doi = {10.1016/j.neuroimage.2025.121443},
pmid = {40915552},
issn = {1095-9572},
abstract = {Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels. Briefly, brain dynamics are captured by temporal first-order derivatives and spatially divided into 'state sets' at each time point based on the velocity and direction of change. This approach transforms the original signals into discrete series consisting of four fundamental states, which efficiently encode individual-specific information. Subsequently, we designed a suite of state-based metrics to quantify regional activities and network interactions. Compared with conventional representations such as resting-state fluctuation amplitude and Pearson's functional connectivity, the state-based representations serve as more discriminative 'brain fingerprints' for individuals and produce reproducible spatial patterns across heterogeneous cohorts (n = 1,015). Regarding functional organization, our proposed profiles extend previous representations into nonlinear domains, revealing not only the canonical default-mode dominant pattern but also patterns dominated by the attention network and basal ganglia. Moreover, we demonstrate that personal phenotypes (such as age and gender) can be decoded from regional representations with high accuracy. The equivalence between state series outperforms other existing network representations in predicting individual fluid intelligence. Overall, this framework establishes a foundation for enriching the repertoire of brain functional representations and enhancing the power of brain-phenotype modeling.},
}

@article {pmid40914696,
year = {2025},
author = {Li, W and Shi, W and Wang, H and Li, J and Chu, C and Zhang, Y and Cui, Y and Cheng, L and Li, K and Lu, Y and Ma, L and Song, M and Yang, Z and Banaschewski, T and Bokde, ALW and Desrivières, S and Flor, H and Grigis, A and Garavan, H and Gowland, P and Walter, H and Brühl, R and Martinot, JL and Martinot, MP and Artiges, E and Nees, F and Orfanos, DP and Lemaitre, H and Poustka, L and Hohmann, S and Millenet, S and Fröhner, JH and Robinson, L and Smolka, MN and Winterer, J and Whelan, R and Fan, L and Jiang, T},
title = {Anatomical connectivity development constrains medial-lateral topography in the dorsal prefrontal cortex.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.08.045},
pmid = {40914696},
issn = {2095-9281},
}

@article {pmid40918004,
year = {2023},
author = {Zong, F and Liu, H and Bai, R and Galvosas, P},
title = {Data inversion of multi-dimensional magnetic resonance in porous media.},
journal = {Magnetic resonance letters},
volume = {3},
number = {2},
pages = {127-139},
pmid = {40918004},
issn = {2772-5162},
abstract = {Since its inception in the 1970s, multi-dimensional magnetic resonance (MR) has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions. MR spectroscopy beyond one dimension allows the study of the correlation, exchange processes, and separation of overlapping spectral information. The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media. Apart from Fourier transform, methods have been developed for processing the multi-dimensional time-domain data, identifying the fluid components, and estimating pore surface permeability via joint relaxation and diffusion spectra. Through the resolution of spectroscopic signals with spatial encoding gradients, multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases. Signals in each voxel are usually expressed as multi-exponential decay, representing microstructures or environments along multiple pore scales. The separation of contributions from different environments is a common ill-posed problem, which can be resolved numerically. Moreover, the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra. This paper reviews the algorithms that have been proposed to process multi-dimensional MR datasets in different scenarios. Detailed information at the microscopic level, such as tissue components, fluid types and food structures in multi-disciplinary sciences, could be revealed through multi-dimensional MR.},
}

@article {pmid40914528,
year = {2025},
author = {Chen, S and Guo, X and Liu, X and Liu, S and Ming, D},
title = {Transdiagnostic homogeneity, and diagnostic-specific biomarkers among major depressive disorder, bipolar disorder and schizophrenia during 40 Hz auditory steady-state response: a normative modeling analysis.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {120189},
doi = {10.1016/j.jad.2025.120189},
pmid = {40914528},
issn = {1573-2517},
abstract = {BACKGROUND: Abnormal gamma-band auditory steady-state response (gamma-ASSR) power has been reported in major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), but distinguishing between these disorders based solely on power remains challenging. Directed functional connectivity (DFC), which captures topological patterns of causal information flow, may provide more diagnostic-specific markers. However, conventional case-control framework often disregards the substantial individual heterogeneity, yielding unreliable biomarkers.

METHODS: An adapted framework integrating DFC heterogeneity with normative modeling was developed. 52 MDD, 33 BD, 39 SZ patients and 107 healthy controls (HC) participated in the 40 Hz-ASSR task. The normative model was established using data from 71 HC to define the population baseline. Thereafter, deviation Z-scores and the proportion of extreme deviations in DFC were calculated.

RESULTS: The DFC deviations showed high individual heterogeneity at most DFCs, with fewer than 2.6 % of individuals exhibiting extreme deviations at the same time point. However, a small proportion of DFC deviations with high overlap were embedded within common connectivity pathways in three disorders, particularly in the frontal and parietal regions. Furthermore, distinct diagnostic-specific patterns were identified: MDD mainly exhibited right temporal-frontal alterations, BD showed a parietal-driven temporo-occipital loop, and SZ presented a midline-centered pyramidal topology linking bilateral temporal-occipital regions. The Z-scores of DFC involved in these diagnostic-specific patterns achieved a maximum accuracy of 99.43 % with the K-nearest neighbors (KNN) algorithm.

CONCLUSIONS: These findings offer novel insights into gamma-ASSR alterations and provide a robust framework for transdiagnostic and disorder-specific identification across MDD, BD, and SZ.},
}

@article {pmid40914440,
year = {2025},
author = {Balam, VP},
title = {Automated EEG Signal Processing: A Comprehensive Investigation into Preprocessing Techniques and Sub-Band Extraction for Enhanced Brain-Computer Interface Applications.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110561},
doi = {10.1016/j.jneumeth.2025.110561},
pmid = {40914440},
issn = {1872-678X},
abstract = {The Electroencephalogram (EEG) is a vital physiological signal for monitoring brain activity and understanding neurological capacities, disabilities, and cognitive processes. Analyzing and classifying EEG signals are key to assessing an individual's reactions to various stimuli. Manual EEG analysis is time-consuming and labor-intensive, necessitating automated tools for efficiency. Machine learning techniques often rely on preprocessing and segmentation methods to integrate automated classification into EEG signal processing, with EEG sub-band components (δ, θ, α, β and γ) playing a crucial role. This paper presents a comprehensive exploration of EEG preprocessing methods, with a specific focus on sub-band extraction techniques used in Brain-Computer Interface (BCI) applications. Various methods-including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, and wavelet transforms (DWT, WPT)-are evaluated through qualitative and quantitative parametric analysis, along with a review of their practical applicability. The study also includes an application-based evaluation using an open-access EEG dataset for drowsiness detection.},
}

@article {pmid40913810,
year = {2025},
author = {Zheng, L and Su, Y and Li, S and Li, X and Zhang, Y and Tseomashko, NE and Sadikovna, AS and Wang, X},
title = {Injectable multifunctional sponges with rough sieve structure and efficient shape-recoverability for small-sized penetrating wound.},
journal = {Journal of colloid and interface science},
volume = {702},
number = {Pt 1},
pages = {138896},
doi = {10.1016/j.jcis.2025.138896},
pmid = {40913810},
issn = {1095-7103},
abstract = {The emergence of special scenarios involving small-sized penetrating wounds has imposed stricter performance requirements on shape-recovery hemostatic materials, particularly regarding their shape fixity and water-triggered shape recovery efficiency. Herein, an efficient shape-recovery sponge dressing with high shape fixity and high-speed liquid absorption, designated as CQT, was developed by integrating a sieve structure with the rough surface coating. The sieve structure, characterized by microporous structures on macroporous walls, enhanced the multi-level and connectivity of the overall pore network, which could improve compressive fixity via enhancing the energy dissipation required for rebound and enabled efficient shape recovery through augmented capillary action during fluid absorption. Concurrently, the enhanced pore connectivity promoted rapid blood absorption (<0.5 s), expanded interfacial contact between blood and hydrophilic pore walls, and improved interception of blood active components, while the rough coating on the pore walls provided more binding sites along with its charge effect to enhance the adhesion and aggregation of blood cells (BCI of 7.8 %). The excellent in vivo hemostatic performance of the sponge (blood loss of 0.31 g and hemostasis time of 63 s) was further validated using a rat liver defect model, suggesting its potential for application in small-sized penetrating wounds. Additionally, this coating has antimicrobial and antioxidant properties that help to prevent infection and reduce inflammation. Thus, the unique sponge dressings possess excellent initial shape adaptability and efficient expansion hemostatic ability, making it very suitable for emergency hemostasis and subsequent repair of small-sized penetrating wounds.},
}

@article {pmid40913768,
year = {2025},
author = {Verwoert, M and Ottenhoff, MC and Tousseyn, S and van Dijk, JP and Kubben, PL and Herff, C},
title = {Moving beyond the motor cortex: A brain-wide evaluation of target locations for intracranial speech neuroprostheses.},
journal = {Cell reports},
volume = {44},
number = {9},
pages = {116241},
doi = {10.1016/j.celrep.2025.116241},
pmid = {40913768},
issn = {2211-1247},
abstract = {Speech brain-computer interfaces (BCIs) offer a solution for those affected by speech impairments by decoding brain activity into speech. Current neuroprosthetics focus on the motor cortex, which might not be suitable for all patient populations. We investigate potential alternative targets for a speech BCI across a brain-wide distribution. Thirty participants are recorded with intracranial electroencephalography during speech production. We continuously predict speech from a brain-wide global to a single-channel local scale, across anatomical features. We find significant speech detection accuracy in both gray and white matter, no significant difference between gyri and sulci, and limited contribution from subcortical areas. Potential targets are located within the depths of and surrounding the lateral fissure bilaterally, such as the (sub)central sulcus, the transverse temporal gyrus, the supramarginal cortex, and parts of the insula. The results highlight the potential benefit of extending beyond the motor cortical surface and reaching the sulcal depth for speech neuroprostheses.},
}

@article {pmid40913530,
year = {2025},
author = {Zhou, E and Wang, X and Liang, J and Liu, Y and Yang, Q and Ran, X and Xia, L and Zou, X and Liu, C and Sun, L and Peng, L and Chen, L and Mao, Y and Wu, Z and Tao, TH and Zhou, Z},
title = {Chronically Stable, High-Resolution Micro-Electrocorticographic Brain-Computer Interfaces for Real-Time Motor Decoding.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e06663},
doi = {10.1002/advs.202506663},
pmid = {40913530},
issn = {2198-3844},
support = {Y2023070//Youth Innovation Promotion Association for Excellent Members/ ; 22QA1410900//Shanghai Rising-Star Program/ ; ZDBS-LY-JSC024//Key Research Program of Frontier Sciences, CAS/ ; JCYJ-SHFY-2022-01//Shanghai Pilot Program for Basic Research-Chinese Academy of Science/ ; 82272116//National Natural Science Foundation of China/ ; 2021SHZDZX//Science and Technology Commission of Shanghai Municipality/ ; 2018AAA0103100//National Major Science and Technology Projects of China/ ; },
abstract = {Brain-computer interfaces (BCIs) enable communication between individuals and computers or other assistive devices by decoding brain activity, thereby reconstructing speech and motor functions for patients with neurological disorders. This study presents a high-resolution micro-electrocorticography (µECoG) BCI based on a flexible, high-density µECoG electrode array, capable of chronically stable and real-time motor decoding. Leveraging micro-nano manufacturing technology, the µECoG BCI achieves a 64-fold increase in electrode density compared to conventional clinical electrode arrays, enhancing spatial resolution while featuring scalability. Over a 203-day in vivo experiment, high-resolution µECoG carrying fine spatial specificity information demonstrated the potential to improve decoding performance while reduce implanted devices size. These advancements provide a pathway to overcome the limitations of conventional ECoG BCIs. During awake surgery, the µECoG BCI enabled game control after 7 min of model training. Furthermore, during practice of 19.87 h, the participant achieved cursor control with a bit rate of 1.13 bits per second (BPS) under full volitional control, and the bit rate reached up to 4.15 BPS with enhanced user interface. These results show that the µECoG BCI achieves comparable performance to intracortical electroencephalographic (iEEG) BCIs without intracortical invasiveness, marking a breakthrough in the clinical feasibility of flexible BCIs.},
}

@article {pmid40913389,
year = {2025},
author = {Ji, Z and Li, L and Zheng, M and Ye, X and Yan, W and Wang, Z and Liu, Y and Wang, Y and Zhang, Y and Zhou, P and Yang, J and Wang, M and Lin, S and Haick, H and Wang, Y},
title = {Conductive Hydrogel-Enabled Electrode for Scalp Electroencephalography Monitoring.},
journal = {Small methods},
volume = {},
number = {},
pages = {e01242},
doi = {10.1002/smtd.202501242},
pmid = {40913389},
issn = {2366-9608},
support = {52303371//National Natural Science Foundation of China/ ; W2521021//National Natural Science Foundation of China/ ; STKJ2023075//Guangdong Science and Technology Department/ ; 2022A1515110209//Guangdong Science and Technology Department/ ; 2021B0301030005//Guangdong Science and Technology Department/ ; GCII-Seed-202406//GTIIT Changzhou Innovation Institute/ ; //Education Foundation of Guangdong Technion-Israel Institute of Technology/ ; //Key Discipline (KD) Fund/ ; //Start-Up fund from Guangdong Technion/ ; },
abstract = {Scalp electroencephalography (EEG) serves as a pivotal technology for the noninvasive monitoring of brain functional activity, diagnosing neurological disorders, and assessing cognitive states. However, inherent compatibility barriers between traditional rigid electrodes and the hairy scalp interface significantly compromise signal quality, long-term monitoring comfort, and user compliance. This review examines conductive hydrogel electrodes' pivotal role in advancing scalp EEG, particularly their unique capacity to overcome hair-interface barriers. The superiority of scalp EEG is first established over forehead/ear EEG for capturing diverse neural signals and defining core requirements for hair-compatible interfaces: scalp conformability, electrical conductivity, low contact impedance, and interfacial stability. Conductive hydrogel electrode applications are then detailed in alpha wave detection, sleep monitoring, event-related potential studies, and brain-computer interfaces. Finally, persisting challenges and future opportunities are discussed.},
}

@article {pmid40913111,
year = {2025},
author = {Duan, C and Ma, S and Chen, M and Wang, J and Jiang, Y and Ye, M and Tan, Y and Cheng, S and Yang, X and Hu, H and Yang, Y and Huang, HF},
title = {Estrogen receptor beta in lateral habenula mediates antidepressant effects of estrogen in postpartum-hormone-withdrawal-induced depression.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40913111},
issn = {1476-5578},
abstract = {Dramatic drop in reproductive hormone, especially estrogen level, from pregnancy to postpartum period is known to contribute to postpartum depression (PPD), but the underlying mechanism and the role of the estrogen receptors (ERs) in this process were unclear. Here, we used an estrogen-withdrawal-induced PPD model following hormone simulated pregnancy (HSP) in female Sprague-Dawley rats to induce depressive-like behaviors. After estrogen withdrawal, we observe an up-regulation of astrocyte-specific potassium channel (Kir4.1) in the brain's anti-reward center lateral habenula (LHb), along with enhanced bursting and excitability of LHb neurons. Among all 3 subtypes of ERs in the LHb, only ERβ shows an HSP-correlated expression temporal dynamics. Systemic administration of selective ERβ agonist, but not agonists of other subtypes of ERs, inhibits neuronal bursting activities and blocks up-regulation of Kir4.1 in the LHb, as well as decreases estrogen-withdrawal-induced depressive-like behavior. Importantly, intra-LHb injection of ERβ agonist is sufficient to rescue depressive-like behaviors induced by estrogen withdrawal. Conversely, local knock-down of ERβ in the LHb suppresses the antidepressant-like effect of estrogen. Our results reveal a critical role of LHb in the pathogenesis of hormone-sensitive PPD and ERβ as a critical mediator of estrogen's antidepressant effects on PPD.},
}

@article {pmid40912944,
year = {2025},
author = {Zhou, J and Li, W and Xu, S and Biswal, BB and Chen, H and Li, J and Liao, W},
title = {Multimodal, multifaceted, imaging-based human brain white matter atlas.},
journal = {Science bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.scib.2025.08.021},
pmid = {40912944},
issn = {2095-9281},
}

@article {pmid40911452,
year = {2025},
author = {Cao, L and Li, H and Dong, Y and Liu, T and Li, J},
title = {Few-Shot Class-Incremental Learning with Dynamic Prototype Refinement for Brain Activity Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3605108},
pmid = {40911452},
issn = {2168-2208},
abstract = {The brain-computer interface (BCI) system facilitates efficient communication and control, with Electroencephalography (EEG) signals as a vital component. Traditional EEG signal classification, based on static deeplearning models, presents a challenge when new classes of the subject's brain activity emerge. The goal is to develop a model that can recognize new few-shot classes while preserving its ability to discriminate between existing ones. This scenario is referred to as Few-Shot Class-Incremental Learning (FSCIL). This work introduces IncrementEEG, a novel framework meticulously designed to tackle the distinct challenges of FSCIL in EEG-based brain activity classification, focusing specifically on emotion recognition and steady-state visual evoked potential (SSVEP). Our work analyzes the role of additive angular margin loss in improving the model's discrimination capabilities. The proposed method is designed to demonstrate robustness in open-world conditions and adaptability to new tasks. Furthermore, we introduce a prototype refinement module comprising a prototype augmentation block and an update block. The prototype augmentation block in the deep feature space preserves the decision boundary for prior tasks, and the prototype update block utilizes a shared embedding space to compute the relation matrix for bootstrapping prototype updates. Extensive experiments conducted across multiple datasets show the superior performance of the IncrementEEG framework compared to state-of-the-art methods. The proposed method advances FSCIL brain activity classification, offering promising potential for applications in Brain-Computer Interface systems.},
}

@article {pmid40911443,
year = {2025},
author = {Zhang, J and Zhu, L and Kong, W and Zhang, J and Cao, J and Cichocki, A},
title = {Reinforcement Learning Decoding Method of Multi-User EEG Shared Information Based on Mutual Information Mechanism.},
journal = {IEEE journal of biomedical and health informatics},
volume = {29},
number = {9},
pages = {6588-6598},
doi = {10.1109/JBHI.2025.3565019},
pmid = {40911443},
issn = {2168-2208},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; *Reinforcement, Psychology ; Brain/physiology ; Adult ; Male ; Young Adult ; Algorithms ; Female ; Deep Learning ; },
abstract = {The multi-user motor imagery brain-computer interface (BCI) is a new approach that uses information from multiple users to improve decision-making and social interaction. Although researchers have shown interest in this field, the current decoding methods are limited to basic approaches like linear averaging or feature integration. They ignored accurately assessing the coupling relationship features, which results in incomplete extraction of multi-source information. To overcome these limitations, we propose a new reinforcement learning electroencephalography (EEG) decoding method based on mutual information mechanisms. Our method enhances the extraction of multi-source common information and uses a dynamic feedback model for inter-brain mutual information reward and punishment mechanisms in the reinforcement learning channel selection module. We feed the single-brain and inter-brain signals after channel selection into deep neural networks, which automatically extract coupled features. Finally, based on the attention indices calculated from EEG signals at prefrontal electrode positions, the output is obtained by voting. Our experimental results show that the average accuracy of dual-brain recognition is improved by 16% compared to single-brain mode. Furthermore, ablation experiments demonstrate that the reinforcement learning module and attention voting module enhance accuracy by 14.5% and 15.7%, respectively.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
*Signal Processing, Computer-Assisted
*Reinforcement, Psychology
Brain/physiology
Adult
Male
Young Adult
Algorithms
Female
Deep Learning

@article {pmid40911279,
year = {2025},
author = {Li, CP and Wang, YY and Zhou, CW and Ding, CY and Teng, P and Nie, R and Yang, SG},
title = {Cutting-edge technologies in neural regeneration.},
journal = {Cell regeneration (London, England)},
volume = {14},
number = {1},
pages = {38},
pmid = {40911279},
issn = {2045-9769},
support = {2024C03028//The Pioneer and Leading Goose R&D Program of Zhejiang Province/ ; 2023R01005//The Leading Innovation and Entrepreneurship Team Program of Zhejiang Province/ ; },
abstract = {Neural regeneration stands at the forefront of neuroscience, aiming to repair and restore function to damaged neural tissues, particularly within the central nervous system (CNS), where regenerative capacity is inherently limited. However, recent breakthroughs in biotechnology, especially the revolutions in genetic engineering, materials science, multi-omics, and imaging, have promoted the development of neural regeneration. This review highlights the latest cutting-edge technologies driving progress in the field, including optogenetics, chemogenetics, three-dimensional (3D) culture models, gene editing, single-cell sequencing, and 3D imaging. Prospectively, the advancements in artificial intelligence (AI), high-throughput in vivo screening, and brain-computer interface (BCI) technologies promise to accelerate discoveries in neural regeneration further, paving the way for more precise, efficient, and personalized therapeutic strategies. The convergence of these multidisciplinary approaches holds immense potential for developing transformative treatments for neural injuries and neurological disorders, ultimately improving functional recovery.},
}

@article {pmid40907818,
year = {2025},
author = {Pan, H and Gao, H and Zhang, Y and Yu, X and Li, Z and Lei, X and Mi, W},
title = {Design and implementation of a writing-stroke motor imagery paradigm for multi-character EEG classification.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.08.058},
pmid = {40907818},
issn = {1873-7544},
abstract = {Motor imagery (MI) based brain-computer interfaces (BCI) decode neural activity to generate command outputs. However, the limited number of distinguishable commands in traditional MI-BCI systems restricts practical applications. To overcome this limitation, we propose a multi-character classification framework based on Electroencephalography (EEG) signals. A structurally simplified MI paradigm for stroke writing is designed, and maximize Euclidean distance trajectory optimization enhances neural separability among five stroke categories. The EEG data cover 11 motor imagery tasks, including five stroke-writing tasks and six related movement tasks such as hand, foot, tongue movements and eye blinks, collected from ten participants. Ensemble Empirical Mode Decomposition (EEMD) eliminates artifact-related Intrinsic Mode Functions (IMFs) and reconstructs the signals. Kernel Principal Component Analysis (KPCA) then conducts nonlinear dimensionality reduction to extract discriminative features. Finally, a recurrent neural network based on Gated Recurrent Units (GRU) performs classification, effectively modeling the temporal dynamics of EEG signals. Experimental results indicate that the optimized stroke paradigm achieves an average classification accuracy of 84.77%, outperforming the unoptimized version at 76.83%. Compared to existing MI-BCI methods, the proposed framework improves classification accuracy and expands the set of distinguishable commands, demonstrating enhanced practicality and effectiveness.},
}

@article {pmid40907530,
year = {2025},
author = {Bryan, MJ and Schwock, F and Yazdan-Shahmorad, A and Rao, RPN},
title = {Temporal basis function models for closed-loop neural stimulation.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae036a},
pmid = {40907530},
issn = {1741-2552},
abstract = {UNLABELLED: Closed-loop neural stimulation provides novel therapies for neurological diseases such as Parkinson's disease (PD), but it is not yet clear whether artificial intelligence (AI) techniques can tailor closed-loop stimulation to individual patients or identify new therapies. Further advancements are required to address a number of difficulties with translating AI to this domain, including sample efficiency, training time, and minimizing loop latency such that stimulation may be shaped in response to changing brain activity.

APPROACH: we propose temporal basis function models (TBFMs) to address these difficulties, and explore this approach in the context of excitatory optogenetic stimulation. We demonstrate the ability of TBF models to provide a single-trial, spatiotemporal forward prediction of the effect of optogenetic stimulation on local field potentials (LFPs) measured in two non-human primates. The simplicity of TBF models allow them to be sample efficient (<20min of training data), rapid to train (<5min), and low latency (<0.2ms) on desktop CPUs.

MAIN RESULTS: we demonstrate the model on 40 sessions of previously published excitatory optogenetic stimulation data. Surprisingly, on test sets it achieved a prediction accuracy 44% higher than a complex nonlinear dynamical systems model that requires hours to train, and 158% higher than a linear state-space model requiring 90 minutes to train. Additionally, in two simulations we show that it successfully allows a closed-loop stimulator to drive neural trajectories, and to achieve the user-preferred trade-offs between under- and over-stimulation, given the uncertainty in the model; it achieves an area under curve (AUC) of 0.7 in both cases.

SIGNIFICANCE: by optimizing for sample efficiency, training time, and latency, our approach begins to bridge the gap between complex AI-based approaches to modeling dynamical systems and the vision of using such forward prediction models to develop novel, clinically useful closed-loop stimulation protocols.},
}

@article {pmid40906512,
year = {2025},
author = {Zhou, L and Zhang, B and Kang, R and Wang, Y and Qin, J and Xiao, Q and Hui, V},
title = {Efficacy of the Conventional Rehabilitation Robot and bio-Signal Feedback-Based Rehabilitation Robot on Upper-Limb Function in Patients with Stroke: A Systematic Review and Network Meta-Analysis.},
journal = {NeuroRehabilitation},
volume = {},
number = {},
pages = {10538135251366668},
doi = {10.1177/10538135251366668},
pmid = {40906512},
issn = {1878-6448},
abstract = {BackgroundWith the development of modern biomedical engineering, bio-signal feedback-based robots, such as electromyography (EMG)-based and brain-computer interface (BCI)-based rehabilitation robot, have emerged beyond conventional designs. However, their comparative effectiveness for improving upper limb function in stroke patients remains unassessed.ObjectiveTo evaluate the comparative effectiveness and ranking of the conventional rehabilitation robot and bio-signal feedback-based rehabilitation robot in improving upper limb function in stroke patients.MethodsPubMed, EMBASE, Cochrane Library, CINAHL, PEDro, EI, IEEEXplore, ClinicalTrials.gov, ICTRP, and ISRCTN Registry were searched for randomized controlled trials (RCTs) from their inception to December 25, 2024. The risk of bias was assessed using the Cochrane Risk of Bias tool (RoB 2.0) and evidence certainty with the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Network meta-analyses were performed using a random-effects model within a frequentist framework.Results59 RCTs with 3,387 participants were included. Based on the surface under the cumulative ranking curve (SUCRA), the BCI-based rehabilitation robot demonstrated the highest overall effects (SUCRA: 99.9%), short-term effects (SUCRA: 99.4%), and long-term effects (SUCRA: 85.1%), though its long-term effects were not significant (mean difference: 2.21; 95% confidence interval: -0.79, 5.21). The EMG-based rehabilitation robot outperformed the conventional rehabilitation robot in short-term interventions (SUCRA: 59.8% vs. 40.3%), but it did not have the same advantage in long-term interventions (SUCRA: 27.1% vs. 66.8%).ConclusionsThe BCI-based rehabilitation robot might be the best choice for improving upper limb function in stroke patients. Future studies should focus on the intervention time for the EMG-based rehabilitation robot.},
}

@article {pmid40904893,
year = {2025},
author = {Patel, N and Verma, J and Jain, S},
title = {Improving EEG classification of alcoholic and control subjects using DWT-CNN-BiGRU with various noise filtering techniques.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1618050},
pmid = {40904893},
issn = {1662-5196},
abstract = {Electroencephalogram (EEG) signal analysis plays a vital role in diagnosing and monitoring alcoholism, where accurate classification of individuals into alcoholic and control groups is essential. However, the inherent noise and complexity of EEG signals pose significant challenges. This study investigates the impact of three signal denoising techniques' Discrete Wavelet Transform(DWT), Discrete Fourier Transform(DFT), and Discrete Cosine Transform (DCT) Non EEG signal classification performance. The motivation behind this study is to identify the most effective preprocessing method for enhancing deep learning model performance in this domain. A novel DWT-CNN-BiGRU model is proposed, which leverages CNN layers for spatial feature extraction and BiGRU layers for capturing temporal dependencies. Experimental results show that the DWT-based approach, combined with standard scaling, achieves the highest accuracy of 94%, with a precision of 0.94, a recall of 0.95, and an F1-score of 0.94. Compared to the baseline DWT-CNN-BiLSTM model, the proposed method provides a modest yet meaningful improvement of approximately 17% in classification accuracy. These findings highlight the superiority of DWT as a preprocessing method and validate the proposed model's effectiveness for EEG-based classification, contributing to the development of more reliable medical diagnostic tools.},
}

@article {pmid40904422,
year = {2025},
author = {Wang, X and Jin, X and Kong, W and Babiloni, F},
title = {CAGCNet: generalized contrastive learning for person identification based on channel aggregated EEG features.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {141},
pmid = {40904422},
issn = {1871-4080},
abstract = {Person identification method based on electroencephalograms (EEG) signals, or so called brainprint recognition is a novel way to distinguish identities with advantages of high security. However, existing methods neglect the distribution difference between training and test data, and the large distance between projected features in the latent space makes the performance of the model degrade in the unseen domain data. In this paper, we propose channel aggregated based generalized contrastive learning framework, which combines multiple modules to overcome this challenge. To capture features from different granularities, we involve multi-scale convolution with channel attention block. In face of distribution of unseen domain, we introduce feature enhancement-based generalized contrast learning to improve the model generalization ability. In the generalized contrast learning module, taking the difficulty of reconstructing EEG signals into consideration, we augment the source domain data at the feature level to improve the generalization ability of the model on the unseen domain data. Extensive experiments on two multi-session datasets shows that our model outperformed other baseline methods, demonstrating its capability of better generalization performance to unseen domain.},
}

@article {pmid40903968,
year = {2025},
author = {Yu, H and Mu, Q and Liu, C and Wang, S and Sun, J},
title = {Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.},
journal = {Neural regeneration research},
volume = {},
number = {},
pages = {},
doi = {10.4103/NRR.NRR-D-25-00217},
pmid = {40903968},
issn = {1673-5374},
abstract = {Electroencephalography-based brain-computer interfaces have revolutionized the integration of neural signals with technological systems, offering transformative solutions across neuroscience, biomedical engineering, and clinical practice. This review systematically analyzes advancements in electroencephalography-based brain-computer interface architectures, emphasizing four pillars, namely signal acquisition, paradigm design, decoding algorithms, and diverse applications. The aim is to bridge the gap between technology and application and guide future research. In signal acquisition, noninvasive systems using wet, dry, and semi-dry electrodes are more comfortable and gentler on the skin compared to traditional methods. However, ensuring stable signal quality over long periods of time remains a challenge. Minimally invasive approaches, such as microneedle arrays and endovascular probes, achieve near-invasive signal fidelity without major surgery. Paradigm design explores task-specific neural encoders. Although motor imagery paradigms are widely used in rehabilitation, they require weeks of user training. Steady-state visually evoked potential and P300 speller paradigms enable rapid calibration, but cause visual and cognitive fatigue. Advanced systems currently combine electroencephalography with electromyography or eye-tracking to better handle real-world tasks. Decoding algorithms have advanced through Riemannian geometry for improved noise filtering, deep learning architectures for automated spatiotemporal feature extraction, and transfer learning frameworks to minimize cross-subject calibration. However, challenges remain in managing inconsistent electroencephalography, reducing processing demands, and ensuring compatibility across different electroencephalography devices. Clinical trials reveal a predominant focus on stroke rehabilitation, while emerging frontiers include astronaut neuromonitoring in space exploration. Challenges include improving signal accuracy, minimizing movement interference, addressing ethical data concerns, and ensuring real-world use. Future advancements focus on biocompatible nanomaterials, adaptive algorithms, and multimodal integration, positioning electroencephalography-based brain-computer interfaces as pivotal tools in next-generation neurotechnology.},
}

@article {pmid40902296,
year = {2025},
author = {Bao, T and Wu, Y and Zhang, H and Cao, J and Wang, J and Liu, J and Fang, J},
title = {Determining microbial extracellular alkaline phosphatase activity in seawater based on surface-enhanced Raman spectroscopy.},
journal = {Marine environmental research},
volume = {212},
number = {},
pages = {107470},
doi = {10.1016/j.marenvres.2025.107470},
pmid = {40902296},
issn = {1879-0291},
abstract = {Microbial extracellular alkaline phosphatase (ALP) plays a significant role in marine phosphorus cycle. Therefore, it is of paramount importance to accurately and rapidly measure ALP activity (APA) in seawater. However, the applications of the existing APA measurement methods are constrained by cumbersome pre-processing, lengthy measurement times, and the influence of colored substances or suspended particles in seawater samples, which limit our accurate understanding of the marine phosphorus cycle. In this study, we developed a sensitive and rapid technique for the quantitative determination of microbial alkaline phosphatase activity in seawater based on surface-enhanced Raman spectroscopy (SERS). This method uses 5-bromo-4-chloro-3-indolyl phosphate (BCIP) as the substrate, and dimethyl sulfoxide (DMSO) as an internal standard to establish a model for quantifying APA in seawater samples. Our results show that the Raman intensity ratio (I600/I700) between the enzymatic reaction product 5-bromo-4-chloro-3-indole (BCI oxide dimers) (I600) and the internal standard (I700) is an ideal quantitation parameter, and there is a strong linear relationship between I600/I700 (y) and APA (x): y = 0.301x + 1.105, R[2] = 0.981. This method is capable of determining APA over a dynamic range of five orders of magnitude (from 0.1 to 10[4] mU L[-1]) with a detection limit of 0.1 mU L[-1]. The reliability of the method is confirmed by comparing the kinetic parameters of the fluorogenic method. Further, this method was tested and successfully applied to quantify APA in coastal and open ocean seawater samples from the Western Pacific Ocean, demonstrating the potential of this method for rapid and reliable detection of APA in the marine environment.},
}

@article {pmid40902051,
year = {2025},
author = {Wang, G and Jiang, L and Song, X and Zhang, Y and Yao, D and Lu, J and Xu, P and Li, F and Liang, Y},
title = {Enhancing Neural Representations of Motor Imagery through Action-Specific Brain Connectivity Patterns.},
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.2025.3605612},
pmid = {40902051},
issn = {1558-0210},
abstract = {Motor imagery (MI) is a cognitive process that allows individuals to mentally simulate movements without physical execution. However, the exploration of functional connectivity (FC) and lateralization mechanisms under different MI actions remains insufficiently understood. In this work, the common orthogonal basis extraction (COBE) algorithm was employed to isolate action-specific components by removing shared background components from the raw FC of the MI process. We demonstrate that action-specific FC effectively captures the hemispheric statistical differences between left- and right-hand MI, outperforming traditional FC and temporal variability measures. And through a comprehensive analysis of network properties at three distinct levels, encompassing the whole-brain network properties, hemispherical properties, and individual nodal strength, complex lateralization patterns associated with diverse types of MI processes were successfully discerned. Furthermore, lateralization indices were further calculated to quantitatively reveal the degree of brain lateralization. Notably, the lateralization performance (LP) derived from action-specific FC exhibited a significant predictive capacity for MI performance, thereby suggesting its potential to evaluate individual MI capability. Collectively, these findings validate the action-specific FC patterns in characterizing neural mechanisms of MI processes and indicate that the LP could potentially be a useful tool to predict the MI performance of MI-based brain-computer inference (BCI), thereby contributing to the formulation of personalized therapeutic strategies for clinical rehabilitation from a new perspective.},
}

@article {pmid40899667,
year = {2025},
author = {Han, NT and Yan, T and Zhuang, R and Kokkinakis, AV and Cao, L},
title = {Sensory Attenuation of Auditory P2 Responses is Modulated by the Sense of Action Timing Control.},
journal = {Psychophysiology},
volume = {62},
number = {9},
pages = {e70134},
doi = {10.1111/psyp.70134},
pmid = {40899667},
issn = {1469-8986},
support = {32271078//National Natural Science Foundation of China/ ; 2023M733124//China Postdoctoral Science Foundation/ ; YJ20220315//China Postdoctoral Science Foundation/ ; 226-2024-00207//Fundamental Research Funds for the Central Universities/ ; },
mesh = {Humans ; Male ; Female ; Electroencephalography ; *Evoked Potentials, Auditory/physiology ; Young Adult ; *Auditory Perception/physiology ; Adult ; Acoustic Stimulation ; *Psychomotor Performance/physiology ; Reaction Time/physiology ; },
abstract = {Sensory attenuation is a well-established phenomenon in which the neurophysiological response elicited by self-initiated stimuli is attenuated compared to identical externally generated stimuli. This phenomenon is mostly studied by comparing the N1 and P2 components of the auditory ERP. Sensory attenuation has also been linked to our sense of agency and control. In the present study, we investigated the role of action timing control in sensory attenuation. Previous studies that investigated the attenuation of the N1/P2 components instructed participants to generate self-initiated stimuli by having the participants perform a series of keypresses while EEG is recorded. ERP responses are then compared to a second condition where participants passively listen to identical sounds. Studies using this paradigm, known as the self-stimulation paradigm, have used a wide range of stimulus onset asynchronies (SOAs) for keypress timing. However, the choice of SOA is rarely explained, perhaps due to an assumption of trial independence. We found that as SOA increased, participants enacted more action timing control to maintain the specified SOA level. The degree of P2 suppression also increased as participants enacted more control. Contrary to most studies in the literature, we did not find N1 suppression but instead found N1 enhancement. The results suggest that P2 suppression may be related to action timing control while N1 enhancement may reflect factors other than motor predictions, in line with more recent interpretations of the N1 suppression effect.},
}

Humans
Male
Female
Electroencephalography
*Evoked Potentials, Auditory/physiology
Young Adult
*Auditory Perception/physiology
Adult
Acoustic Stimulation
*Psychomotor Performance/physiology
Reaction Time/physiology

@article {pmid40899634,
year = {2025},
author = {McGill, K and Bhullar, N and Carrandi, A and Batterham, PJ and Wayland, S and Maple, M},
title = {A Randomized Controlled Trial of an SMS-Based Brief Contact Intervention for People Bereaved by Suicide.},
journal = {Suicide & life-threatening behavior},
volume = {55},
number = {5},
pages = {e70043},
doi = {10.1111/sltb.70043},
pmid = {40899634},
issn = {1943-278X},
support = {//Suicide Prevention Australia/ ; },
mesh = {Humans ; Female ; Male ; *Bereavement ; *Text Messaging ; Adult ; *Suicide/psychology ; Middle Aged ; Suicidal Ideation ; Help-Seeking Behavior ; Psychological Distress ; Resilience, Psychological ; Young Adult ; },
abstract = {INTRODUCTION: Brief contact interventions (BCI) refer to short messages delivered proactively to a specific target population. The aim of this study was to test the effectiveness of a mobile phone short-message service (SMS) BCI for people bereaved by suicide.

METHODS: Participants were randomly allocated. The BCI group received text messages over a 6-week period. The active control group received the intervention website. Pre- and post-intervention surveys assessed demographic, suicide exposure and five key outcomes (psychological distress, suicidal ideation, complicated grief, resilience, and professional help-seeking intentions). BCI participants were also invited to participate in an interview post-intervention.

RESULTS: Of 99 participants randomized, 52 BCI and 47 control completed pre-intervention surveys. Post-intervention response rates were low (BCI: n = 15; 28.85%; active control: n = 16; 34.04%), with no statistically significant changes in key outcome measures. Eight BCI participants completed follow-up interviews. Relevance, timing of support, benefit to bereavement, and recommendations for scaling were identified.

CONCLUSIONS: Recruitment and retention challenges meant the effectiveness of the BCI could not be statistically determined. Qualitative evidence supported BCI acceptability for people bereaved by suicide. Recommendations to improve the intervention include embedding the BCI within existing postvention services offered soon after a death occurs and tailoring of messages to individuals' needs.

TRIAL REGISTRATION: This trial was registered with the Australian New Zealand Clinical Trial Register (ACTRN12621001430820).},
}

Humans
Female
Male
*Bereavement
*Text Messaging
Adult
*Suicide/psychology
Middle Aged
Suicidal Ideation
Help-Seeking Behavior
Psychological Distress
Resilience, Psychological
Young Adult

@article {pmid40898814,
year = {2025},
author = {Zhao, Y and Lu, P and Wang, X and Yin, M},
title = {Bidirectional optimization of firing rate in a mouse neuronal brain-machine interface.},
journal = {Biology letters},
volume = {21},
number = {9},
pages = {20250176},
doi = {10.1098/rsbl.2025.0176},
pmid = {40898814},
issn = {1744-957X},
support = {//High-level Talent Project of Natural Science Foundation of Hainan Province/ ; //Sanya Yazhou Bay Science and Technology City/ ; //'Rising Star of South China Sea' Project of Hainan Province/ ; //National Natural Science Foundation of China/ ; //STI 2030-Major Projects/ ; },
mesh = {Animals ; *Brain-Computer Interfaces ; Mice ; Reward ; *Neurons/physiology ; Male ; *Motor Cortex/physiology ; *Neuronal Plasticity ; Mice, Inbred C57BL ; Feedback, Sensory ; },
abstract = {Neuroplasticity enables the brain to adapt neural activity, but whether this can be harnessed for abstract optimization tasks like seeking curve extrema remains unclear. Here, we used a brain-machine interface in mice, pairing auditory feedback of neuronal firing rate with water rewards, to investigate whether motor cortex neurons can optimize activity along a unimodal curve ([Formula: see text]). The curve maps firing rate ([Formula: see text]) to sound frequency increase speed ([Formula: see text]), where the curve extremum accelerates reward acquisition. Over conditioning sessions, mice learned to modulate firing rates towards this peak, reducing reward time from 18.64 ± 7.30 s to 11.59 ± 4.38 s and increasing high-response events from 66 to 104 occurrences. Putative neurons increasingly prioritized high-response intervals, with positive proportion increments in upper intervals versus negative trends in lower ones. These findings demonstrate that cortical neurons can dynamically optimize activity along non-monotonic reward landscapes, revealing neuroplasticity as a substrate for adaptive self-optimization. This expands our understanding of how the brain learns abstract rules via feedback, with implications for neuroprosthetic design that leverage neural adaptability.},
}

Animals
*Brain-Computer Interfaces
Mice
Reward
*Neurons/physiology
Male
*Motor Cortex/physiology
*Neuronal Plasticity
Mice, Inbred C57BL
Feedback, Sensory

@article {pmid40898635,
year = {2025},
author = {Isakova, EV and Kotov, SV and Borisova, VA},
title = {[Effectiveness of "brain-computer" interfaces with biofeedback in the rehabilitation of cognitive impairment after a stroke].},
journal = {Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova},
volume = {125},
number = {8. Vyp. 2},
pages = {54-60},
doi = {10.17116/jnevro202512508254},
pmid = {40898635},
issn = {1997-7298},
mesh = {Humans ; Male ; *Brain-Computer Interfaces ; Female ; Middle Aged ; *Stroke/complications/psychology ; *Stroke Rehabilitation/methods ; *Biofeedback, Psychology ; Aged ; *Cognitive Dysfunction/rehabilitation/etiology ; Electroencephalography ; Adult ; Neuropsychological Tests ; },
abstract = {OBJECTIVE: Comparison of the effectiveness of two "brain-computer" interface (BCI) software complexes using biofeedback (BF) and standard therapy in restoring cognitive functions after a stroke.

MATERIAL AND METHODS: Eighty-nine stroke patients were examined. Neuropsychological testing was carried out using the Montreal Cognitive Assessment Scale (MoCA), the Tracking test, the Wechsler subtest 9 Kohs block design test, the Schulte tables, the Memorization of 10 Words test (according to A.R. Luria). Using the simple randomization method, three groups were formed: the main group (n=37), the comparison group (n=33) and the control group (n=19). In Group 1, sessions were conducted with BCI+BF based on the rhythm P300; in Group 2, with BCI+BF based on the mu-rhythm of electroencephalography (EEG), Group 3 received standard therapy.

RESULTS: An increase in the total MoCA score was reported in all three groups. The results in Groups 1 and 2 were comparable, exceeding those in Group 3 (p1-2=0.199, p1-3<0.001, p2-3=0.037). The effectiveness in Group 1 did not depend on the baseline MoCA score, exceeding the indicators in Group 3; in Group 2, the advantage over Group 3 was with a baseline MoCA of at least 22. According to the Schulte tables and the Tracking test, comparable statistically significant changes were obtained in Groups 1 and 2; no statistically significant change was reported in the control group. The Kohs block design test showed a more statistically significant change in the main group. The Memorization of 10 Words test by A.R. Luria also showed a more consistent improvement in mnestic disorders in the main group.

CONCLUSION: The effectiveness of BCI+BF exceeded standard therapy for post-stroke cognitive impairment. The advantage of IMC+BFB used in the main group over IMC+BFB in the comparison group was noted, which was due to a decrease in the effectiveness of the latter with a baseline MoCA score of less than 22 points, lower performance in the Memorizing 10 Words test and the Kohs block design test.},
}

Humans
Male
*Brain-Computer Interfaces
Female
Middle Aged
*Stroke/complications/psychology
*Stroke Rehabilitation/methods
*Biofeedback, Psychology
Aged
*Cognitive Dysfunction/rehabilitation/etiology
Electroencephalography
Adult
Neuropsychological Tests

@article {pmid40898590,
year = {2025},
author = {Paveliev, M and Melnikova, A and Egorchev, AA and Parpura, V and Aganov, AV},
title = {Neuroimplants and the Glial Scar: What Makes the Brain-Computer Link Work?.},
journal = {Journal of neurochemistry},
volume = {169},
number = {9},
pages = {e70203},
doi = {10.1111/jnc.70203},
pmid = {40898590},
issn = {1471-4159},
support = {24-75-00123//Russian Science Foundation/ ; },
mesh = {Humans ; Animals ; *Brain-Computer Interfaces/trends ; *Cicatrix/pathology/prevention & control ; *Neuroglia/pathology ; *Brain/pathology ; Tissue Engineering/methods ; *Brain Injuries/therapy/pathology ; },
abstract = {Neuroimplants are likely major technological breakthroughs of the next decade with the potential for unprecedented social impact. In addition to attractive and miracle-looking possibilities, the major obstacle for the industry is complicated, unpredictable, and unfavorable side effects due to tissue damage, biocompatibility limitations, and foreign body response at the brain-implant interface. Luckily, one major barrier preventing the connection of the neuroimplant to brain cells-the glial scar-has been studied previously for its role in brain trauma. This review highlights pharmacological and tissue engineering avenues that could be readily transferred from the brain trauma area to fast-growing neuroimplant engineering. The opportunities for chondroitinase ABC treatment, stem cells, and hydrogels for the prevention of glial scarring are emphasized. Alternatively, the glial scar may also be viewed not as an obstacle but as a possible regeneration-permissive component of the optimally working brain-neuroimplant interface. Feasible steps in that direction are discussed, including applications for chondroitin sulfate-binding peptides. Finally, the crucial role of new microscopy and data processing techniques for peri-implant glial scar monitoring is highlighted. To that end, we stress the importance of artificial intelligence, including artificial neuronal networks, for the analysis of cell morphology at the brain-neuroimplant interface.},
}

Humans
Animals
*Brain-Computer Interfaces/trends
*Cicatrix/pathology/prevention & control
*Neuroglia/pathology
*Brain/pathology
Tissue Engineering/methods
*Brain Injuries/therapy/pathology

@article {pmid40897729,
year = {2025},
author = {Sakakibara, Y and Kusutomi, T and Kondoh, S and Etani, T and Shimada, S and Imamura, Y and Naruse, Y and Fujii, S and Ibaraki, T},
title = {A Nostalgia Brain-Music Interface for enhancing nostalgia, well-being, and memory vividness in younger and older individuals.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {32337},
pmid = {40897729},
issn = {2045-2322},
mesh = {Humans ; *Music/psychology ; Male ; Female ; Adult ; Aged ; Electroencephalography ; Middle Aged ; *Brain/physiology ; Young Adult ; Mental Recall/physiology ; *Emotions/physiology ; *Neurofeedback/methods ; Auditory Perception/physiology ; *Memory/physiology ; Memory, Episodic ; },
abstract = {Music-evoked nostalgia has the potential to assist in recalling autobiographical memories and enhancing well-being. However, nostalgic music preferences vary from person to person, presenting challenges for applying nostalgia-based music interventions in clinical settings, such as a non-pharmacological approach. To address these individual differences, we developed the Nostalgia Brain-Music Interface (N-BMI), a neurofeedback system that recommends nostalgic songs tailored to each individual. This system is based on prediction models of nostalgic feelings, developed by integrating subjective nostalgia ratings, acoustic features and in-ear electroencephalographic (EEG) data during song listening. To test the effects of N-BMI on nostalgic feelings, state-level well-being, and memory recall, seventeen older and sixteen younger participants took part in the study. The N-BMI was personalized for each individual, and songs were recommended under two conditions: the "nostalgic condition", where songs were selected to enhance nostalgic feelings, and the "non-nostalgic condition", to reduce nostalgic feelings. We found nostalgic feelings, state-level well-being, and subjective memory vividness were significantly higher after listening to the recommended songs in the nostalgic condition compared to the non-nostalgic condition in both groups. This indicates that the N-BMI enhanced nostalgic feelings, state-level well-being, and memory recall across both groups. The N-BMI paves the way for innovative therapeutic interventions, including non-pharmacological approaches.},
}

Humans
*Music/psychology
Male
Female
Adult
Aged
Electroencephalography
Middle Aged
*Brain/physiology
Young Adult
Mental Recall/physiology
*Emotions/physiology
*Neurofeedback/methods
Auditory Perception/physiology
*Memory/physiology
Memory, Episodic

@article {pmid40897258,
year = {2025},
author = {Morozova, M and Yakovlev, L and Syrov, N and Lebedev, M and Kaplan, A},
title = {Cortical responses to tactile imagery: a high-density EEG study of the μ-rhythm event-related desynchronization and somatosensory evoked potentials.},
journal = {NeuroImage},
volume = {319},
number = {},
pages = {121440},
doi = {10.1016/j.neuroimage.2025.121440},
pmid = {40897258},
issn = {1095-9572},
abstract = {Tactile imagery (TI) engages somatosensory cortices in both hemispheres, along with widespread brain regions associated with the imagery process itself. Actively simulating touch can influence the processing of actual tactile stimuli, as reflected by modulations in somatosensory evoked potentials (SEPs) components. This study uses high-density electroencephalography (EEG) and sLORETA-based source localization to analyse cortical sources of SEPs components susceptible to active skin sensations imagery. Twenty healthy participants performed TI and tactile attention (TA) tasks. TI enhanced early SEP components (P100), indicating engagement of primary somatosensory cortical networks. This was accompanied with robust μ-rhythm event-related desynchronization (ERD) localized to the postcentral gyrus. While TA also elicited μ-ERD, its cortical distribution was broader, suggesting involvement of more distributed and possibly non-specific attentional mechanisms. Notably, sensor-space analysis revealed an enhanced late frontal P200 peak during TI, potentially indicating increased frontal activation. However, source-space analysis confirmed the absence of frontal pole involvement in SEPs during TI, underscoring the importance of accurate source localization. Thus, TI was found to significantly activate primary somatosensory cortices, influencing early stages of real tactile stimulus processing. Its effects were more spatially focused compared to those induced by TA. These findings suggest that TI could be a prospective approach for sensorimotor rehabilitation. On the other hand, TA could provide an effortless method for modulating sensorimotor rhythms in BCI control.},
}

@article {pmid40896338,
year = {2025},
author = {Liu, M},
title = {Editorial: Neural dynamics for brain-inspired control and computing: advances and applications.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1666218},
doi = {10.3389/fnins.2025.1666218},
pmid = {40896338},
issn = {1662-4548},
}

@article {pmid40896268,
year = {2025},
author = {Lee, D and Byun, K and Lee, S},
title = {Enhancing cognitive function through blood flow restriction: An effective resistance exercise modality for middle-aged women.},
journal = {Journal of exercise science and fitness},
volume = {23},
number = {4},
pages = {379-388},
doi = {10.1016/j.jesf.2025.08.002},
pmid = {40896268},
issn = {1728-869X},
abstract = {PURPOSE: Cognitive decline progresses more rapidly in women than in men, with a higher prevalence of neurodegenerative diseases observed in females. Exercise has been shown to enhance cognitive function through the upregulation of neurotrophic factors such as brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF) and insulin-like growth factor-1 (IGF-1). However, high-load resistance exercise may not be suitable for all populations, particularly middle-aged women. Low-load resistance exercise with blood flow restriction (LLBFR) has emerged as an effective alternative. This study investigated the acute effects of LLBFR on neurotrophic factors and cognitive function in middle-aged women.

METHODS: Fifteen healthy middle-aged women completed a randomized crossover trial involving four conditions: control (CON), low-load resistance exercise (LLRE), LLBFR, and moderate-load resistance exercise (MLRE). Cognitive function was assessed before and after each session using the color-word matching Stroop task (CWST). Blood samples were analyzed for serum levels of BDNF, VEGF, and IGF-1, and lactate concentrations were measured to evaluate metabolic responses.

RESULTS: Only the LLBFR condition showed significant improvements in CWST reaction time (p = 0.002) with no changes in error rates, indicating enhanced cognitive performance. Serum BDNF and VEGF levels increased significantly following both LLBFR (p < 0.001, p = 0.014, respectively) and MLRE (p < 0.001, p = 0.004, respectively), whereas IGF-1 levels remained unchanged across conditions. Increases in lactate concentrations were positively correlated with changes in BDNF and VEGF (p < 0.001 for both), but not with IGF-1.

CONCLUSION: A single session of LLBFR improved cognitive function and upregulated neurotrophic factors, particularly BDNF and VEGF, in middle-aged women. These findings suggest that LLBFR may be an effective intervention for promoting cognitive health in this population.},
}

@article {pmid40894778,
year = {2025},
author = {Tong, JQ and Binder, JR and Conant, LL and Mazurchuk, S and Anderson, AJ and Fernandino, L},
title = {A Common Representational Code for Event and Object Concepts in the Brain.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.08.22.671793},
pmid = {40894778},
issn = {2692-8205},
abstract = {Events and objects are two fundamental ways in which humans conceptualize their experience of the world. Despite the significance of this distinction for human cognition, it remains unclear whether the neural representations of object and event concepts are categorically distinct or, instead, can be explained in terms of a shared representational code. We investigated this question by analyzing fMRI data acquired from human participants (males and females) while they rated their familiarity with the meanings of individual words (all nouns) denoting object and event concepts. Multivoxel pattern analyses indicated that both categories of lexical concepts are represented in overlapping fashion throughout the association cortex, even in the areas that showed the strongest selectivity for one or the other type in univariate contrasts. Crucially, in these areas, a feature-based model trained on neural responses to individual event concepts successfully decoded object concepts from their corresponding activation patterns (and vice versa), showing that these two categories share a common representational code. This code was effectively modeled by a set of experiential feature ratings, which also accounted for the mean activation differences between these two categories. These results indicate that neuroanatomical dissociations between events and objects emerge from quantitative differences in the cortical distribution of more fundamental features of experience. Characterizing this representational code is an important step in the development of theory-driven brain-computer interface technologies capable of decoding conceptual content directly from brain activity.},
}

@article {pmid40894619,
year = {2025},
author = {Spalding, Z and Duraivel, S and Rahimpour, S and Wang, C and Barth, K and Schmitz, C and Lad, SP and Friedman, AH and Southwell, DG and Viventi, J and Cogan, GB},
title = {Shared latent representations of speech production for cross-patient speech decoding.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2025.08.21.671516},
pmid = {40894619},
issn = {2692-8205},
abstract = {Speech brain-computer interfaces (BCIs) can restore communication in individuals with neuromotor disorders who are unable to speak. However, current speech BCIs limit patient usability and successful deployment by requiring large volumes of patient-specific data collected over long periods of time. A promising solution to facilitate usability and accelerate their successful deployment is to combine data from multiple patients. This has proven difficult, however, due to differences in user neuroanatomy, varied placement of electrode arrays, and sparse sampling of targeted anatomy. Here, by aligning patient-specific neural data to a shared latent space, we show that speech BCIs can be trained on data combined across patients. Using canonical correlation analysis and high-density micro-electrocorticography (μECoG), we uncovered shared neural latent dynamics with preserved micro-scale speech information. This approach enabled cross-patient decoding models to achieve improved accuracies relative to patient-specific models facilitated by the high resolution and broad coverage of μECoG. Our findings support future speech BCIs that are more accurate and rapidly deployable, ultimately improving the quality of life for people with impaired communication from neuromotor disorders.},
}

@article {pmid40893910,
year = {2025},
author = {Teng, J and Cho, S and Lee, SM},
title = {Tri-manual interaction in hybrid BCI-VR systems: integrating gaze, EEG control for enhanced 3D object manipulation.},
journal = {Frontiers in neurorobotics},
volume = {19},
number = {},
pages = {1628968},
doi = {10.3389/fnbot.2025.1628968},
pmid = {40893910},
issn = {1662-5218},
abstract = {Brain-computer interface (BCI) integration with virtual reality (VR) has progressed from single-limb control to multi-limb coordination, yet achieving intuitive tri-manual operation remains challenging. This study presents a consumer-grade hybrid BCI-VR framework enabling simultaneous control of two biological hands and a virtual third limb through integration of Tobii eye-tracking, NeuroSky single-channel EEG, and non-haptic controllers. The system employs e-Sense attention thresholds (>80% for 300 ms) to trigger virtual hand activation combined with gaze-driven targeting within 45° visual cones. A soft maximum weighted arbitration algorithm resolves spatiotemporal conflicts between manual and virtual inputs with 92.4% success rate. Experimental validation with eight participants across 160 trials demonstrated 87.5% virtual hand success rate and 41% spatial error reduction (σ = 0.23 mm vs. 0.39 mm) compared to traditional dual-hand control. The framework achieved 320 ms activation latency and 22% NASA-TLX workload reduction through adaptive cognitive load management. Time-frequency analysis revealed characteristic beta-band (15-20 Hz) energy modulations during successful virtual limb control, providing neurophysiological evidence for attention-mediated supernumerary limb embodiment. These findings demonstrate that sophisticated algorithmic approaches can compensate for consumer-grade hardware limitations, enabling laboratory-grade precision in accessible tri-manual VR applications for rehabilitation, training, and assistive technologies.},
}

@article {pmid40892657,
year = {2025},
author = {Song, Z and Zhang, X and Li, M and Tan, J and Wang, Y},
title = {Online Neural-to-Movement Mapping Transfer for Task Switching and Retention in Brain-Machine Interfaces.},
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.2025.3605246},
pmid = {40892657},
issn = {1558-0210},
abstract = {Current brain-machine interfaces (BMIs) often rely on decoders trained for single tasks, limiting their flexibility in real-world applications. We propose an online learning framework that enables the transfer of neural-to-movement (knowledge) across tasks, supporting both task switching and memory retention. In our framework, neural activity is projected into a dynamical jPCA space to effectively dissociate into variant and invariant components. The variant components of the neural patterns are then aligned by deriving Gradient-based Kullback-Leibler Divergence Minimization (GKLD) for efficient online adaptation. A kernel reinforcement learning (KRL) model then decodes aligned neural signals while reusing prior neural-to-movement mapping. Evaluated on rats switching between a one-lever pressing and a two-lever discrimination task, the framework shows rapid convergence, over four times faster than the baseline method, and improves decoding accuracy by around 35% during task switching. Furthermore, when switching back to the original task, the framework successfully retains knowledge from the old task. Our method demonstrates general applicability to multiple task switching scenarios and maintains stable decoding across three representative days over a 21-day period, highlighting its potential for long-term, real-world use.},
}

@article {pmid40890094,
year = {2025},
author = {Du, Z and Chu, C and Shi, W and Luo, N and Lu, Y and Wang, H and Zhao, B and Xiong, H and Yang, Z and Jiang, T},
title = {Connectome-constrained ligand-receptor interaction analysis for understanding brain network communication.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {8179},
pmid = {40890094},
issn = {2041-1723},
support = {62403465//National Natural Science Foundation of China (National Science Foundation of China)/ ; GZC20232999//China Postdoctoral Science Foundation/ ; 2024M753502//China Postdoctoral Science Foundation/ ; },
mesh = {*Connectome/methods ; *Brain/physiology/diagnostic imaging/metabolism ; Humans ; Algorithms ; Ligands ; *Nerve Net/physiology/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; },
abstract = {Both diffusion magnetic resonance imaging and transcriptomic technologies have provided unprecedented opportunities to dissect brain network communication, offering insights from structural connectivity and signaling molecules separately. However, incorporating these complementary modalities for characterizing the interregional communication remains challenging. By simplifying the communication processes into an optimal transport problem, which is defined as the ligand-receptor expression coupling constrained by structurally-derived communication cost, we develop a method called CLRIA (connectome-constrained ligand-receptor interaction analysis) to infer a low-rank representation of ligand-receptor interaction-mediated communication networks. To solve the proposed optimization problem, the block majorization minimization algorithm is adopted and proven to converge globally. We benchmark CLRIA on simulated and published data, validating its accuracy and computational efficiency. Subsequently, we demonstrate the CLRIA's utility in evaluating communication strategies and asymmetric communication using its solution. Furthermore, CLRIA-derived communication patterns successfully decode brain state transitions. Overall, our results highlight CLRIA as a valuable tool for understanding complex communication in the brain.},
}

*Connectome/methods
*Brain/physiology/diagnostic imaging/metabolism
Humans
Algorithms
Ligands
*Nerve Net/physiology/diagnostic imaging
Diffusion Magnetic Resonance Imaging

@article {pmid40887906,
year = {2025},
author = {Zhao, Y and Sun, R and Wang, Z and Ma, S and Wang, R and Li, F and Geng, H},
title = {Engineered Hydrogels as Functional Components in Controllable Neuromodulation for Translational Therapeutics.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.5c01269},
pmid = {40887906},
issn = {2576-6422},
abstract = {Controllable neuromodulation leveraging multimodal triggers synergized with hydrogels represents a transformative therapeutic strategy for pro-regenerative neural repair. Strategic incorporation of programmable neuromodulatory interventions and engineered hydrogels within localized neural niches is critical for clinical translation, characterized by lower invasiveness and greater therapeutic efficacy. This review elucidates the physiochemical features of hydrogels, systematically classifying hydrogel-based neuromodulation into five distinct modes (electrical, ionic, biomechanical, optical, and biochemical) and highlighting the intrinsic multidimensional structural and chemical engineering employed to enhance neuromodulatory performance. Key principles of hydrogel design and fabrication are provided from the perspective of tissue-implant interactions, such as mechanical compatibility, electrointegration, adhesion, and wireless activation. Hydrogels embedded with low-impedance organic and inorganic components, such as conductive polymers and noble metals, are investigated to provide high-level evidence to enable precise cellular stimulation for intrinsic nerve repair, neural prosthesis, and brain-machine interface. Crucially, this review highlights the synergistic integration of these principles into multimodal, closed-loop systems, which combine functions like electrophysiological sensing with on-demand biochemical release for intelligent, autonomous therapies. Finally, this review confronts the critical challenges for clinical translation and discusses future directions, including the potential of artificial intelligence-driven materials design to accelerate the development of next-generation neural interfaces.},
}

@article {pmid40887182,
year = {2025},
author = {Li, S and Fu, Y and Zhang, Y and Lu, G},
title = {[Research on fatigue recognition based on graph convolutional neural network and electroencephalogram signals].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {686-692},
doi = {10.7507/1001-5515.202410003},
pmid = {40887182},
issn = {1001-5515},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Fatigue/diagnosis/physiopathology ; *Automobile Driving ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Convolutional Neural Networks ; },
abstract = {Electroencephalogram (EEG) serves as an effective indicator of detecting fatigue driving. Utilizing the open accessible Shanghai Jiao Tong University Emotion Electroencephalography Dataset (SEED-VIG), driving states are divided into three categories including awake, tired and drowsy for investigation. Given the characteristics of mutual influence and interdependence among EEG channels, as well as the consistency of the graph convolutional neural network (GCNN) structure, we designed an adjacency matrix based on the Pearson correlation coefficients of EEG signals among channels and their positional relationships. Subsequently, we developed a GCNN for recognition. The experimental results show that the average classification accuracy of driving state categories for 20 subjects, from the SEED-VIG dataset under the smooth feature of differential entropy (DE) linear dynamic system is 91.66%. Moreover, the highest classification accuracy can reach 98.87%, and the average Kappa coefficient is 0.83. This work demonstrates the reliability of this method and provides a guideline for the research field of safe driving brain computer interface.},
}

Humans
*Electroencephalography/methods
*Neural Networks, Computer
*Fatigue/diagnosis/physiopathology
*Automobile Driving
Brain-Computer Interfaces
Signal Processing, Computer-Assisted
Convolutional Neural Networks

@article {pmid40887181,
year = {2025},
author = {Xiao, N and Li, M},
title = {[Motor imagery classification based on dynamic multi-scale convolution and multi-head temporal attention].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {678-685},
doi = {10.7507/1001-5515.202408051},
pmid = {40887181},
issn = {1001-5515},
mesh = {*Electroencephalography/methods ; Humans ; *Neural Networks, Computer ; *Brain-Computer Interfaces ; *Attention ; Signal Processing, Computer-Assisted ; *Imagination/physiology ; Algorithms ; },
abstract = {Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA). The model first applies multi-band filtering to the raw MI-EEG signals and inputs the results into the feature extraction module. Then, it uses a dynamic multi-scale CNN to capture temporal features while adjusting attention weights, followed by spatial convolution to extract spatiotemporal feature sequences. Next, the model further optimizes temporal correlations through time dimensionality reduction and a multi-head attention mechanism to generate more discriminative features. Finally, MI classification is completed under the supervision of cross-entropy loss and center loss. Experiments show that the proposed model achieves average accuracies of 80.32% and 90.81% on BCI Competition IV datasets 2a and 2b, respectively. The results indicate that DMSCMHTA can adaptively extract personalized spatiotemporal features and outperforms current mainstream methods.},
}

*Electroencephalography/methods
Humans
*Neural Networks, Computer
*Brain-Computer Interfaces
*Attention
Signal Processing, Computer-Assisted
*Imagination/physiology
Algorithms

@article {pmid40887179,
year = {2025},
author = {Pang, Z and Wang, Y and Dong, Q and Cheng, Z and Li, Z and Zhang, R and Cui, H and Chen, X},
title = {[Research on hybrid brain-computer interface based on imperceptible visual and auditory stimulation responses].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {660-667},
doi = {10.7507/1001-5515.202504033},
pmid = {40887179},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Acoustic Stimulation ; *Photic Stimulation ; Electroencephalography ; Evoked Potentials, Auditory/physiology ; Adult ; },
abstract = {In recent years, hybrid brain-computer interfaces (BCIs) have gained significant attention due to their demonstrated advantages in increasing the number of targets and enhancing robustness of the systems. However, Existing studies usually construct BCI systems using intense auditory stimulation and strong central visual stimulation, which lead to poor user experience and indicate a need for improving system comfort. Studies have proved that the use of peripheral visual stimulation and lower intensity of auditory stimulation can effectively boost the user's comfort. Therefore, this study used high-frequency peripheral visual stimulation and 40-dB weak auditory stimulation to elicit steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR) signals, building a high-comfort hybrid BCI based on weak audio-visual evoked responses. This system coded 40 targets via 20 high-frequency visual stimulation frequencies and two auditory stimulation frequencies, improving the coding efficiency of BCI systems. Results showed that the hybrid system's averaged classification accuracy was (78.00 ± 12.18) %, and the information transfer rate (ITR) could reached 27.47 bits/min. This study offers new ideas for the design of hybrid BCI paradigm based on imperceptible stimulation.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Acoustic Stimulation
*Photic Stimulation
Electroencephalography
Evoked Potentials, Auditory/physiology
Adult

@article {pmid40887178,
year = {2025},
author = {Fu, Y and Lu, H},
title = {[Technical maturity and bubble risks of brain-computer interface (BCI): Considerations from research to industrial translation].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {42},
number = {4},
pages = {651-659},
doi = {10.7507/1001-5515.202507053},
pmid = {40887178},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual ; Electroencephalography ; Event-Related Potentials, P300 ; },
abstract = {Brain-computer interface (BCI) technology faces structural risks due to a misalignment between its technological maturity and industrialization expectations. This study used the Technology Readiness Level (TRL) framework to assess the status of major BCI paradigms-such as steady-state visual evoked potential (SSVEP), motor imagery, and P300-and found that they predominantly remained at TRL4 to TRL6, with few stable applications reaching TRL9. The analysis identified four interrelated sources of bubble risk: overly broad definitions of BCI, excessive focus on decoding performance, asynchronous translational progress, and imprecise terminology usage. These distortions have contributed to the misallocation of research resources and public misunderstanding. To foster the sustainable development of BCI, this paper advocated the establishment of a standardized TRL evaluation system, clearer terminological boundaries, stronger support for fundamental research, enhanced ethical oversight, and the implementation of inclusive and diversified governance mechanisms.},
}

*Brain-Computer Interfaces
Humans
Evoked Potentials, Visual
Electroencephalography
Event-Related Potentials, P300

@article {pmid40886590,
year = {2025},
author = {Zhu, S and Cao, T and He, Q and Wang, N and Jia, Y and Chai, X and Yang, Y},
title = {Advanced neuroimaging techniques to decipher brain connectivity networks in patients with disorder of consciousness: a narrative review.},
journal = {NeuroImage. Clinical},
volume = {48},
number = {},
pages = {103864},
doi = {10.1016/j.nicl.2025.103864},
pmid = {40886590},
issn = {2213-1582},
abstract = {Advanced neuroimaging techniques have revolutionized our ability to decode brain networks in patients with disorders of consciousness (DoC), offering unprecedented insights into the structural and functional underpinnings of consciousness impairment. This review systematically examines and summarizes the clinical applications of modern neuroimaging methodologies-specifically functional MRI and diffusion MRI- for DoC patients from three key perspectives: (1) pathogenic mechanism and theory evolution, (2) accurate diagnosis and prognosis assessment, and (3) treatment strategy and efficacy evaluation. By integrating network neuroscience with clinical insights, we highlight the transformative role of neuroimaging in unraveling network-level damage, refining clinical assessments, and guiding therapeutic innovations. We further outline the potential applicational challenges associated with leveraging neuroimaging techniques to advance both scientific research on consciousness networks and clinical practice in DoC management, hoping to better address these complex conditions.},
}

@article {pmid40885826,
year = {2025},
author = {Vargas-Irwin, CE and Hosman, T and Gusman, JT and Pun, TK and Simeral, JD and Singer-Clark, T and Kapitonava, A and Nicolas, C and Shah, NP and Avansino, DT and Kamdar, F and Williams, ZM and Henderson, JM and Hochberg, LR},
title = {Gesture encoding in human left precentral gyrus neuronal ensembles.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {1315},
pmid = {40885826},
issn = {2399-3642},
support = {U01DC017844, R01DC014034//U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; T32MH115895//U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)/ ; UH2NS095548, U01NS123101//U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; 19CSLOI34780000//American Heart Association (American Heart Association, Inc.)/ ; },
mesh = {Humans ; *Gestures ; *Motor Cortex/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Female ; *Neurons/physiology ; Hand/physiology ; Middle Aged ; Spinal Cord Injuries/physiopathology ; },
abstract = {Understanding the cortical activity patterns driving dexterous upper limb motion has the potential to benefit a broad clinical population living with limited mobility through the development of novel brain-computer interface (BCI) technology. The present study examines the activity of ensembles of motor cortical neurons recorded using microelectrode arrays in the dominant hemisphere of two BrainGate clinical trial participants with cervical spinal cord injury as they attempted to perform a set of 48 different hand gestures. Although each participant displayed a unique organization of their respective neural latent spaces, it was possible to achieve classification accuracies of ~70% for all 48 gestures (and ~90% for sets of 10). Our results show that single-unit ensemble activity recorded in a single hemisphere of human precentral gyrus has the potential to generate a wide range of gesture-related signals across both hands, providing an intuitive and diverse set of potential command signals for intracortical BCI use.},
}

Humans
*Gestures
*Motor Cortex/physiology
Brain-Computer Interfaces
Male
Adult
Female
*Neurons/physiology
Hand/physiology
Middle Aged
Spinal Cord Injuries/physiopathology

@article {pmid40883960,
year = {2025},
author = {Wang, Z and Tan, S and Lu, K and Li, Q and Jiao, B and Li, W and Wu, X and Zhang, L and Zeng, L and Bai, R},
title = {The contributions of aquaporin-4 to water exchange across the blood-brain barrier measured by filter-exchange imaging.},
journal = {Magnetic resonance in medicine},
volume = {},
number = {},
pages = {},
doi = {10.1002/mrm.70049},
pmid = {40883960},
issn = {1522-2594},
support = {2024SSYS0019//Key R&D Program of Zhejiang Province/ ; 82172050//National Natural Science Foundation of China/ ; 82222032//National Natural Science Foundation of China/ ; 92359303//National Natural Science Foundation of China/ ; 2022ZD0206000//STI2030-Major Projects of China/ ; },
abstract = {PURPOSE: Water exchange across the blood-brain barrier (WEXBBB) is a promising biomarker for assessing the blood-brain barrier (BBB) integrity. However, the physiological mechanisms governing WEXBBB remain unclear. This study was conducted to investigate the contribution of Na[+]/K[+]-ATPase (NKA) on the luminal side of endothelial cells and aquaporin-4 (AQP4) to WEXBBB.

METHODS: WEXBBB was measured using filter-exchange imaging for BBB assessment (FEXI-BBB) on rats, and data were fitted using an adapted two-compartment crusher-compensated exchange rate (CCXR) model. Test-retest reliability of the vascular water efflux rate constant (kbo) was assessed. Ouabain and 2-(nicotinamide)-1,3,4-thiadiazole (TGN-020) were administered to inhibit NKA on the luminal side of endothelial cells and AQP4, respectively, to investigate their roles in WEXBBB measured by FEXI-BBB.

RESULTS: Fixing intravascular diffusivity in the two-compartment CCXR model significantly improved estimation accuracy and precision of kbo and other parameters. The test-retest experiment demonstrated that this method had good reproducibility in measuring kbo (intraclass correlation coefficient = 0.79). Administering TGN-020, which inhibits AQP4, significantly decreased kbo by 32% (kbo = 3.07 ± 0.81 s[-1] vs. 2.09 ± 1.10 s[-1], p < 0.05). However, the ouabain-treated group showed no significant change in kbo compared with that of the control group (2.51 ± 0.58 s[-1] vs. 2.37 ± 1.02 s[-1], p = 0.73) in the NKA inhibition experiment.

CONCLUSIONS: WEXBBB decreased by 32% after administering TGN-020, but no downward trend was noted after administering ouabain. Our findings indicate that AQP4 expression/function, but not NKA activity on the luminal side of endothelial cells, plays a significant role in regulating WEXBBB.},
}

@article {pmid40883792,
year = {2025},
author = {Wan, C and Zhang, Q and Qiu, Y and Zhang, W and Nie, Y and Zeng, S and Wang, J and Shen, X and Yu, C and Wu, X and Zhang, Y and Li, Y},
title = {Effects of dual-task mode brain-computer interface based on motor imagery and virtual reality on balance and attention in patients with stroke: a randomized controlled pilot trial.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {187},
pmid = {40883792},
issn = {1743-0003},
support = {No. JBGS202414//Jiangsu Provincial People's Hospital, Clinical Diagnosis and Treatment Technology Innovation 'Open bidding for selecting the best candidates' Project/ ; No. ST242102//Major sports research projects of Jiangsu Sports Bureau/ ; 2024TGYY51//Ministry of Industry and Information Technology and National Health Commission High-end Equipment Promotion and Application Project/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; Middle Aged ; Female ; *Stroke Rehabilitation/methods ; Pilot Projects ; Single-Blind Method ; *Postural Balance/physiology ; *Virtual Reality ; *Attention/physiology ; Aged ; Adult ; *Stroke/physiopathology/psychology ; *Imagination/physiology ; *Imagery, Psychotherapy/methods ; },
abstract = {BACKGROUND: Brain-computer interface (BCI) has been shown to be beneficial in improving lower limb motility in stroke, but their effectiveness on balance and attention is unclear. In addition, current BCIs are mostly in single-task mode. The BCI system used in this study was based on a dual-task model of motor imagery (MI) and virtual reality (VR). Previous studies have demonstrated that dual-task seems to be beneficial for balance and attention. The purpose of this study was to validate the effects of MI-VR-based dual-task BCI on balance and attention in participants with stroke.

METHODS: This pilot, single-blind, randomized controlled trial involved 38 stroke participants, randomized to the BCI (BCI pedaling training) or control group (conventional pedaling). Both groups trained 20 min daily, 5 days a week for 4 weeks, alongside conventional rehabilitation. Thirty participants completed the program (mean age: 56.56 years, mean disease duration: 4.48 months). Assessments were made before and after 4 weeks. The primary outcome was the Berg Balance Scale (BBS), and secondary outcomes included the Timed Up and Go Test (TUGT), Fugl-Meyer Lower Extremity Assessment (FMA-LE), Symbol Digit Modalities Test (SDMT), and average attention index.

RESULTS: 30 participants completed the study (14 in the BCI and 16 in the control group). The retention rates were 73.68% and 84.21% respectively. No adverse events were reported in this study and participants did not report any discomfort. The changes in BBS, TUGT and SDMT values in the BCI group were significantly better than those in the control group (P < 0.05). Average attention index of the BCI group's participants grew with the number of training sessions, and there was a significant difference comparing pre- to post-treatment (p < 0.05). The value of BBS change is linearly correlated with the value of SDMT change (F = 8.778, y = 0.59x + 1.90, P < 0.001).

CONCLUSIONS: This study initially showed positive effects of dual-task mode of BCI pedalling training on balance and attention in stroke participants. However, given the preliminary nature of this study and its limitations, the results need to be treated with caution. Trial registration Chinese Clinical Trial Registry Identifier: ChiCTR2300071522. Registered on 2023/05/17.},
}

Humans
*Brain-Computer Interfaces
Male
Middle Aged
Female
*Stroke Rehabilitation/methods
Pilot Projects
Single-Blind Method
*Postural Balance/physiology
*Virtual Reality
*Attention/physiology
Aged
Adult
*Stroke/physiopathology/psychology
*Imagination/physiology
*Imagery, Psychotherapy/methods

@article {pmid40883351,
year = {2025},
author = {Metwalli, D and Kiroles, AE and Radwan, YA and Mohamed, EA and Barakat, M and Ahmed, A and Omar, AM and Selim, S},
title = {ArEEG: an Open-Access Arabic Inner Speech EEG Dataset.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {1513},
pmid = {40883351},
issn = {2052-4463},
mesh = {*Electroencephalography ; Humans ; *Brain-Computer Interfaces ; *Speech ; Language ; },
abstract = {Recent advancements in Brain-Computer Interface (BCI) technology are shifting towards inner speech over motor imagery due to its intuitive nature and broader command spectrum, enhancing interaction with electronic devices. However, the reliance on a large number of electrodes in available datasets complicates the development of cost-effective BCIs. Additionally, the lack of publicly available datasets hinder the development of this technology. To address this, we introduce a new Arabic Inner Speech dataset, featuring five distinct classes, exceeding the typical four-class datasets, and recorded using only eight electrodes, making it an economical solution. Our primary objective is to provide an open-access, multi-class Electroencephalographic (EEG) dataset in Arabic for inner speech, encompassing five commands. This dataset is designed to enhance our understanding of brain activity, facilitate the integration of BCI technologies in Arabic-speaking regions, and serve as a valuable resource for developing and testing real-world BCI applications. Through this contribution, we aim to bridge the gap between language-specific neural data and the field of neurotechnology, fostering innovation and inclusivity in BCI research.},
}

*Electroencephalography
Humans
*Brain-Computer Interfaces
*Speech
Language

@article {pmid40881516,
year = {2025},
author = {Swarnakar, R},
title = {Brain-Computer Interfaces in Rehabilitation: Implementation Models and Future Perspectives.},
journal = {Cureus},
volume = {17},
number = {7},
pages = {e88873},
doi = {10.7759/cureus.88873},
pmid = {40881516},
issn = {2168-8184},
abstract = {Brain-computer interfaces (BCIs) represent an emerging advancement in rehabilitation, enabling direct communication between the brain and external devices to aid recovery in individuals with neurological impairments. BCIs can be classified into invasive, semi-invasive, non-invasive, or hybrid types. By interpreting neural signals and converting them into control commands, BCIs can bypass damaged pathways, offering therapeutic potential for conditions such as stroke, spinal cord injury, traumatic brain injury, and neurodegenerative diseases such as amyotrophic lateral sclerosis. BCIs' current applications, such as motor restoration via robotic exoskeletons and functional electrical stimulation, cognitive enhancement through neurofeedback and attention training, and communication tools for individuals with severe physical limitations, are largely being explored within research settings and are not yet part of routine clinical practice. Advances in EEG signal acquisition, machine learning, wearable and wireless systems, and integration with virtual reality are enhancing the clinical utility of BCIs by improving accuracy, adaptability, and usability. However, widespread clinical adoption faces challenges, including signal variability, training complexity, data privacy, and ethical and regulatory issues. Ethical challenges in BCI include issues related to the ownership and misuse of brain data, risks of neural interference, threats to autonomy and personal identity, as well as concerns around data privacy, user consent, emotional manipulation, and accountability in neural interventions. In this context, this editorial has also proposed one model (NEURO model checklist) for BCI implementation in rehabilitation. The future of BCIs in rehabilitation lies in developing personalized, closed-loop, and home-based systems, enabled by interdisciplinary collaboration among clinicians, engineers, neuroscientists, and policymakers. With continued research and ethical implementation, BCIs have the potential to transform neurorehabilitation and greatly enhance patient outcomes and quality of life.},
}

@article {pmid40881023,
year = {2025},
author = {Zhang, S and Lu, Z and Zhang, B and Zhang, Y and Liang, Z and Zhang, L and Li, L and Huang, G and Zhang, Z and Li, Z},
title = {Graph-based feature learning methods for subject-dependent and subject-independent motor imagery EEG decoding.},
journal = {Cognitive neurodynamics},
volume = {19},
number = {1},
pages = {139},
doi = {10.1007/s11571-025-10291-5},
pmid = {40881023},
issn = {1871-4080},
abstract = {UNLABELLED: The significant intra-individual variability and inter-individual differences in scalp electroencephalogram (EEG) make it difficult to learn task-distinguishable features, posing a challenge for motor imagery brain-computer interfaces. Current feature learning methods often produce an incomplete feature space, struggling to accommodate these variations and differences. Additionally, the weak discriminative nature of this feature space results in diminished EEG classification performance. This paper introduces novel graph-based feature learning methods to improve motor imagery decoding performance in both subject-dependent and subject-independent contexts. Firstly, construct a complete time-frequency-spatial-graph (TFSG) feature space. The original EEG signals are segmented into multiple time-frequency units using filter banks and sliding time windows. Spatial and brain network-based graph features are then extracted from each time-frequency unit and fused to create the TFSG features. This fused feature space is larger and more inclusive, effectively accommodating both intra- and inter-individual EEG variations. Secondly, learn a discriminative TFSG feature space. Two advanced methods are proposed. The first method employs a nonconvex sparse optimization model with log function regularization, which reduces bias in model estimation, thereby enabling more accurate learning of EEG patterns. The second method incorporates Fisher's criterion regularization into a sparse optimization framework to improve feature separability. A unified algorithmic framework is developed to solve the two new models. Our methods are validated on two motor imagery EEG datasets, achieving the highest average classification accuracies of 82.93, 68.52, and 71.69% for subject-dependent, subject-independent, and subject-adaptive evaluation methods, respectively. Experimental results demonstrate that the developed TFSG features significantly enhance both subject-dependent and subject-independent decoding performance, while the proposed regularization models improve the discriminability of the feature space, leading to further advancements in motor imagery decoding performance.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10291-5.},
}

@article {pmid40880337,
year = {2025},
author = {Kim, J and Kim, SP},
title = {A Plug-and-Play P300-Based BCI with Zero-Training Application.},
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.2025.3603979},
pmid = {40880337},
issn = {1558-0210},
abstract = {The practical deployment of P300-based brain-computer interfaces (BCIs) has long been hindered by the need for user-specific calibration and multiple stimulus repetitions. In this study, we build and validate a plug-and-play, zero-training P300 BCI system that operates in a single-trial setting using a pre-trained xDAWN spatial filter and a deep convolutional neural network. Without any subject-specific adaptation, participants could control an IoT device via the BCI system in real time, with decoding accuracy reaching 85.2% comparable to the offline benchmark of 87.8%, demonstrating the feasibility of realizing a plug-and-play BCI. Offline analyses revealed that a small set of parietal and occipital electrodes contributed most to decoding performance, supporting the viability of low-density, high-accuracy BCI configurations. A data sufficiency simulation provided quantitative guidelines for pre-training dataset size, and an error trial analysis showed that both stimulus timing and preparatory attentional state influenced real-time decoding performance. Together, these results demonstrate the real-time validation of a fully pre-trained, zero-training P300 BCI operating on a single-trial basis, without stimulus repetition or user-specific calibration, and offer practical insights for developing scalable, robust, and user-friendly BCI systems.},
}

@article {pmid40880336,
year = {2025},
author = {Li, X and Wang, X and Chen, S and Zhu, W and Jin, R and Peng, W},
title = {Gamma-Band Binaural Beats Neuromodulation Enhances P300 Classification in an Auditory Brain-Computer Interface Paradigm.},
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.2025.3604016},
pmid = {40880336},
issn = {1558-0210},
abstract = {While established neuromodulation techniques like transcranial magnetic stimulation and transcranial direct current stimulation have shown potential for enhancing brain-computer interface (BCI) performance, their clinical adoption faces challenges including high implementation costs, technical complexity, and safety concerns. This study investigated binaural beats (BB), a non-invasive auditory neuromodulation method characterized by operational simplicity and minimal adverse effects, as a practical alternative for optimizing auditory P300-BCI. Employing a crossover experimental design, thirty healthy participants underwent gamma-band (40 Hz) and alpha-band (10 Hz) BB stimulation in separate sessions. Auditory oddball paradigm experiments were conducted before and after each BB intervention. Electroencephalogram (EEG) data were decoded using both a machine learning classifier and a deep learning model for P300 classification. Additionally, irregular-resampling auto-spectral analysis (IRASA) was applied to extract aperiodic components from EEG during BB stimulation to evaluate changes in brain state. The results demonstrated frequency-dependent modulation effects: gamma-BB significantly improved P300 classification accuracy while alpha-BB impaired performance. Neurophysiological analysis revealed that gamma-BB decreased the aperiodic exponent, indicating enhanced brain arousal level, whereas alpha-BB produced the opposite pattern. Importantly, the aperiodic parameter change showed a significant association with BCI performance improvement. These findings established gamma-BB as an effective, low-cost neuromodulation strategy for augmenting auditory P300-BCI through brain state modulation.},
}

@article {pmid40880333,
year = {2025},
author = {Xu, M and Zhang, B and Zhang, L and Wang, D and Chen, Y},
title = {A Decade of Rapid Serial Visual Presentation Paradigm in Brain-Computer Interface for Target Detection: Current Status and Trends.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2025.3603945},
pmid = {40880333},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalography (EEG)-based Rapid Serial Visual Presentation (RSVP) has steadily gained attention since 2015 as a paradigm to enhance image target detection in brain-computer interfaces (BCIs) used with healthy individuals.

METHODS: We reviewed the literature using Scopus and Web of Science as primary databases, covering publications from 2015 to 2024. After literature screening and filtering, a total of 86 papers on RSVP-BCI studies were analyzed over this decadelong period. The research categorizes RSVP into three dimensions: public datasets, paradigm encoding, and decoding methods, while exploring eight mode combinations involving target types, subject groups, and different modalities.

RESULTS: Our literature search revealed a scarcity of studies addressing diverse target types across different subject groups or modality combinations, indicating a promising direction for future RSVP-BCI development. Future efforts should prioritize inclusivity across all age groups, the design of user-friendly stimulus interfaces, and the development of advanced algorithms, with the goal of creating a more widely accessible RSVP-BCI system.

CONCLUSION: We have provided a comprehensive review of advances over the past decade in RSVP-based target detection, including datasets, encoding design, and decoding methods and potential applications.

SIGNIFICANCE: The present work aims to articulate prospective trajectories for the continued advancement of the RSVP community.},
}

@article {pmid40878633,
year = {2025},
author = {Jiang, H and Ren, B and Zhang, Y and Zhou, Y and Wu, J and Yu, X and Yu, H and Ni, P and Xu, Y and Deng, W and Guo, W and Hu, X and Qi, X and Li, T},
title = {Alterations of plasma neural-derived extracellular vesicles microRNAs in patients with bipolar disorder.},
journal = {Psychological medicine},
volume = {55},
number = {},
pages = {e256},
doi = {10.1017/S0033291725000741},
pmid = {40878633},
issn = {1469-8978},
mesh = {Humans ; *Bipolar Disorder/genetics/blood/metabolism ; *Extracellular Vesicles/metabolism ; Female ; Male ; *MicroRNAs/metabolism/blood/genetics ; Adult ; Middle Aged ; Microglia/metabolism ; Case-Control Studies ; Prefrontal Cortex/metabolism ; },
abstract = {BACKGROUND: MicroRNAs (miRNAs) alterations in patients with bipolar disorder (BD) are pivotal to the disease's pathogenesis. Since obtaining brain tissue is challenging, most research has shifted to analyzing miRNAs in peripheral blood. One innovative solution is sequencing miRNAs in plasma extracellular vesicles (EVs), particularly those neural-derived EVs emanating from the brain.

METHODS: We isolated plasma neural-derived EVs from 85 patients with BD and 39 healthy controls (HC) using biotinylated antibodies targeting a neural tissue marker, followed by miRNA sequencing and expression analysis. Furthermore, we conducted bioinformatic analyses and functional experiments to delve deeper into the underlying pathological mechanisms of BD.

RESULTS: Out of the 2,656 neural-derived miRNAs in EVs identified, 14 were differentially expressed between BD patients and HC. Moreover, the target genes of miR-143-3p displayed distinct expression patterns in the prefrontal cortex of BD patients versus HC, as sourced from the PsychENCODE database. The functional experiments demonstrated that the abnormal expression of miR-143-3p promoted the proliferation and activation of microglia and upregulated the expression of proinflammatory factors, including IL-1β, IL-6, and NLRP3. Through weighted gene co-expression network analysis, a module linking to the clinical symptoms of BD patients was discerned. Enrichment analyses unveiled these miRNAs' role in modulating the axon guidance, the Ras signaling pathway, and ErbB signaling pathway.

CONCLUSIONS: Our findings provide the first evidence of dysregulated plasma miRNAs within neural-derived EVs in BD patients and suggest that neural-derived EVs might be involved in the pathophysiology of BD through related biological pathways, such as neurogenesis and neuroinflammation.},
}

Humans
*Bipolar Disorder/genetics/blood/metabolism
*Extracellular Vesicles/metabolism
Female
Male
*MicroRNAs/metabolism/blood/genetics
Adult
Middle Aged
Microglia/metabolism
Case-Control Studies
Prefrontal Cortex/metabolism

@article {pmid40877476,
year = {2025},
author = {Wu, Y and Qian, B and Li, T and Qin, Y and Guan, Z and Chen, T and Jia, Y and Zhang, P and Zeng, D and Moroi, S and Raman, R and Thinggaard, BS and Pedersen, F and Ñehe, JAO and Kamalden, TA and Zhou, Y and Jin, Y and Li, H and Ran, AR and Yang, D and Meng, Z and Peng, Q and Zheng, YF and Wang, D and Ji, H and Zang, P and Yin, C and Shen, J and Chen, Y and Yu, W and Dai, R and Zhang, C and Zhao, X and Wang, X and Chen, Y and Wu, Q and Xie, H and Szeto, SKH and Chan, JYY and Chan, VTT and Xie, HT and Wei, R and Li, J and Ma, W and Zhu, L and Wang, H and Fu, H and Wang, W and Lin, S and Xu, Z and Guan, N and Zhang, X and Grzybowski, A and Gołębiowska-Bogaj, M and Gawęcki, M and Smedowski, A and Szaraniec, W and Wu, Y and Wen, Y and Chen, X and Yao, Y and , and Lim, LL and Cheung, CY and Tan, GSW and Grauslund, J and Ruamviboonsuk, P and Sivaprasad, S and Keane, PA and Wang, YX and Tham, YC and Cheng, CY and Wong, TY and Sheng, B},
title = {An eyecare foundation model for clinical assistance: a randomized controlled trial.},
journal = {Nature medicine},
volume = {},
number = {},
pages = {},
pmid = {40877476},
issn = {1546-170X},
support = {82388101//National Natural Science Foundation of China (National Science Foundation of China)/ ; IS23096//Natural Science Foundation of Beijing Municipality (Beijing Natural Science Foundation)/ ; },
abstract = {In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision-language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets. Efficacy validation invited 44 ophthalmologists across North America, Europe, Asia and Africa in primary and specialty care settings, highlighting its utility as a clinical copilot. The RCT-a parallel, single-center, double-masked study-assessed EyeFM as a clinical copilot in retinal disease screening among a high-risk population in China. A total of 668 participants (mean age 57.5 years, 79.5% male) were randomized to 16 ophthalmologists, equally allocated into intervention (with EyeFM copilot) and control (standard care) groups. The primary endpoint indicated that ophthalmologists with EyeFM copilot achieved higher correct diagnostic rate (92.2% versus 75.4%, P < 0.001) and referral rate (92.2% versus 80.5%, P < 0.001). Secondary outcome indicated improved standardization score of clinical reports (median 33 versus 37, P < 0.001). Participant satisfaction with the screening was similar between groups, whereas the intervention group demonstrated higher compliance with self-management (70.1% versus 49.1%, P < 0.001) and referral suggestions (33.7% versus 20.2%, P < 0.001) at follow-up. Post-deployment evaluations indicated strong user acceptance. Our study provided evidence that implementing EyeFM copilot can improve the performance of ophthalmologists and the outcome of patients. Chinese Clinical Trial Registry registration: ChiCTR2500095518 .},
}

@article {pmid40877466,
year = {2025},
author = {Fan, YS and Xu, Y and Hettwer, MD and Yang, P and Sheng, W and Wang, C and Yang, M and Kirschner, M and Valk, SL and Chen, H},
title = {Neurodevelopmentally rooted epicenters in schizophrenia: sensorimotor-association spatial axis of cortical thickness alterations.},
journal = {Molecular psychiatry},
volume = {},
number = {},
pages = {},
pmid = {40877466},
issn = {1476-5578},
abstract = {Pathological disturbances in schizophrenia have been suggested to propagate via the functional and structural connectome across the lifespan. However, how the connectome guides early cortical reorganization of developing schizophrenia remains unknown. Here, we used early-onset schizophrenia (EOS) as a neurodevelopmental disease model to investigate putative early pathologic origins propagating through the functional and structural connectome. We compared 95 patients with antipsychotic-naïve first-episode EOS and 99 typically developing controls (total n = 194; 120 females; 7-17 years of age). While patients showed widespread cortical thickness reductions, thickness increases were observed in primary cortical areas. Using normative connectomics models, we found that epicenters of thickness reductions were located in association regions linked to language, affective, and cognitive functions, while epicenters of thickness increases in EOS were located in sensorimotor regions subserving visual, somatosensory, and motor functions. Using post-mortem transcriptomic data of six donors, we observed that the epicenter map differentiated oligodendrocyte-related transcriptional changes at its sensory apex, whereas the association end was related to the expression of excitatory/inhibitory neurons. More generally, the epicenter map was associated with dysregulation of neurodevelopmental disorder genes and human accelerated region genes, suggesting potential common genetic determinants across diverse neurodevelopmental conditions. Taken together, our results highlight the developmentally rooted pathological origins of schizophrenia and its transcriptomic overlap with other neurodevelopmental disorders.},
}

@article {pmid40876460,
year = {2025},
author = {Marino, PJ and Bahureksa, L and Fisac, CF and Oby, ER and Smoulder, AL and Motiwala, A and Degenhart, AD and Grigsby, EM and Joiner, WM and Chase, SM and Yu, BM and Batista, AP},
title = {A posture subspace in the primary motor cortex.},
journal = {Neuron},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuron.2025.07.030},
pmid = {40876460},
issn = {1097-4199},
abstract = {To generate movements, the brain must combine information about movement goal and body posture. The motor cortex (primary motor cortex [M1]) is a key node for the convergence of these information streams. How are posture and goal signals organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures. We found that posture- and goal-related components of neural population activity were separable and resided in nearly orthogonal subspaces. The posture subspace was stable across tasks. Within each task, neural trajectories for each goal had similar shapes across postures. Our results reveal a simpler organization of posture signals in M1 than previously recognized. The compartmentalization of posture and goal signals might allow the two to be flexibly combined in the service of our broad repertoire of actions.},
}

@article {pmid40876238,
year = {2025},
author = {Azati, Y and Wang, X and Ye, X and Zhang, K},
title = {Refining the classification of combined alignment sections on mountainous freeways and analyzing the spatio-temporal effects on crash frequency.},
journal = {Accident; analysis and prevention},
volume = {221},
number = {},
pages = {108222},
doi = {10.1016/j.aap.2025.108222},
pmid = {40876238},
issn = {1879-2057},
abstract = {Combined alignment sections of mountainous freeways often feature complex geometric configurations-such as downhill sag/convex curves, slope-changing curves, and uphill curves-that significantly affect crash risk. Existing studies typically apply homogeneous segmentation and broad classifications (e.g., downhill, uphill, sag/convex), which fail to capture the specific effects of geometric combinations on crash frequency. In addition, traffic operations and weather conditions in mountainous areas exhibit strong seasonal variation, and using annual data may obscure important patterns, making quarterly analysis necessary. The interaction of complex geometry, dynamic traffic flow, and adverse winter weather results in nonlinear spatio-temporal effects that conventional models cannot effectively capture. To address this, the study integrates road geometry, traffic operation, and environmental data into a Zero-Inflated Negative Binomial (ZINB) model enhanced with Gaussian processes, systematically analyzing the nonlinear spatio-temporal effects on crash frequency. Results show that the proposed model outperforms spatial- or temporal-only models in prediction accuracy (RMSE = 0.566) and model fit (LOOIC = 5961.2), with the variance of spatio-temporal interaction effects estimated at 1.35 (95 % BCI: 1.12-1.58), indicating substantial nonlinear influence. Key findings include a 56 % increase in crash frequency on straight downhill sag curves, a 2 % reduction on straight uphill convex curves, an 80.3 % increase for every additional 1,000 vehicles in daily traffic flow, and a 28.8 % decrease in crash frequency for each 1 °C rise in temperature. The study presents a refined classification and modeling framework that significantly improves crash risk identification and prediction for mountainous freeways, offering strong support for traffic safety management.},
}

@article {pmid40876195,
year = {2025},
author = {Liu, Z and Hong, Q and Huang, L and Sha, L and Peng, A and Chen, L},
title = {Women with epilepsy during pregnancy: A systematic review of current guidelines.},
journal = {Epilepsy & behavior : E&B},
volume = {171},
number = {},
pages = {110658},
doi = {10.1016/j.yebeh.2025.110658},
pmid = {40876195},
issn = {1525-5069},
abstract = {OBJECTIVE: To systematically evaluate the quality of existing guidelines for the management of pregnancy in women with epilepsy (WWE) and compare their key recommendations.

METHODS: A systematic review of available clinical practice guidelines and expert consensus statements was conducted. The quality of the literature was assessed using the Appraisal of Guidelines for Research & Evaluation II (AGREE II) instrument. Core information was extracted using a predefined form and subjected to comparative analysis.

RESULTS: Only 14 guidelines on WWE pregnancy management have been published worldwide. Most guidelines performed well in scope definition, clarity of purpose, and presentation, but the evidence base was relatively weak. Recommendations were largely consistent across guidelines regarding preconception counseling, folic acid supplementation, vaginal delivery, breastfeeding, and avoidance of valproate. However, discrepancies were observed in the selection of certain antiseizure medications (ASMs), therapeutic drug monitoring, and the timing and dosage of folic acid supplementation. Current guidelines lack recommendations on newer ASMs and antinociceptive management during delivery.

CONCLUSION: The variability in recommendations among WWE pregnancy management guidelines reflects the insufficiency of the existing evidence base, highlighting the need for enhanced methodological rigor in guideline development and more comprehensive, evidence-based recommendations. Establishing large-scale prospective pregnancy registries is critical for improving WWE pregnancy management guidelines.},
}

@article {pmid40875414,
year = {2025},
author = {Korik, A and Bois, ND and Bornot, JS and McShane, N and Guger, C and Felice, AD and Lennon, O and Coyle, D},
title = {Decoding the variable velocity of lower-limb stepping movements from EEG.},
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.2025.3603635},
pmid = {40875414},
issn = {1558-0210},
abstract = {Accurate decoding of lower-limb movement from electroencephalography (EEG) is essential for developing brain-computer interface (BCI) controlled exoskeletons in neurorehabilitation. This study investigates 3D velocity decoding at three fibular anatomical markers during overground stepping in healthy participants (N=9), using two approaches: (1) linear regression (LR) and (2) a deep learning (DL) framework combining convolutional neural networks (CNNs) and long short-term memory (LSTM) units. Participants were divided into two groups: G1 (n=5) performed cued forward and self-paced backward steps; G2 (n=4) performed cued forward and backward steps. The DL model significantly outperformed LR, achieving highest decoding accuracy (DA) in the forward-backward direction at the fibular head (R = 0.63±0.06, M±SD). Topographical analysis identified dominant contributions from the sensorimotor cortex (coupled with frontal regions in G2) within the 8-40 Hz band. Functional connectivity (FC) analysis revealed significant differences: only G2 showed statistically significant FC (p<0.05), likely reflecting increased cognitive and sensorimotor demands under dual-cue conditions. In G2, FC occurred across delta (0-4 Hz), theta (4-8 Hz), alpha/mu (8-12 Hz), and low-beta (12-18 Hz) bands, linking motor areas associated with lower- and upper-limb control to other cortical regions, including the middle temporal gyrus (MTG), superior frontal gyrus (SFG), posterior cingulate cortex (PCC), superior parietal lobule (SPL), and supramarginal gyrus (SMG). These findings demonstrate that EEG-based 3D decoding of lower-limb kinematics is feasible during realistic locomotor tasks and suggest that cortical synchronization patterns vary with movement context. Our CNN-LSTM framework may support adaptive, intent-driven exoskeleton development for personalized neurorehabilitation.},
}

@article {pmid40875138,
year = {2025},
author = {Zhou, Q and Song, J and Zhao, Y and Zhang, S and Du, Q and Ke, L},
title = {IF-MMCL: an individual focused network with multi-view and multi-modal contrastive learning for cross-subject emotion recognition.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40875138},
issn = {1741-0444},
abstract = {Electroencephalography (EEG) usage in emotion recognition has garnered significant interest in brain-computer interface (BCI) research. Nevertheless, in order to develop an effective model for emotion identification, features need to be extracted from EEG data in terms of multi-view. In order to tackle the problems of multi-feature interaction and domain adaptation, we suggest an innovative network, IF-MMCL, which leverages multi-modal data in multi-view representation and integrates an individual focused network. In our approach, we build an individual focused network with multi-view that utilizes individual focused contrastive learning to improve model generalization. The network employs different structures for multi-view feature extraction and uses multi-feature relationship computation to identify the relationships between features from various views and modalities. Our model is validated using four public emotion datasets, each containing various emotion classification tasks. In leave-one-subject-out experiments, IF-MMCL performs better than the previous methods in model generalization with limited data.},
}

@article {pmid40874066,
year = {2025},
author = {Tarara, P and Przybył, I and Schöning, J and Gunia, A},
title = {Motor imagery-based brain-computer interfaces: an exploration of multiclass motor imagery-based control for Emotiv EPOC X.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1625279},
pmid = {40874066},
issn = {1662-5196},
abstract = {INTRODUCTION: Enhancing the command capacity of motor imagery (MI)-based brain-computer interfaces (BCIs) remains a significant challenge in neuroinformatics, especially for real-world assistive applications. This study explores a multiclass BCI system designed to classify multiple MI tasks using a low-cost EEG device.

METHODS: A BCI system was developed to classify six mental states: resting state, left and right hand movement imagery, tongue movement, and left and right lateral bending, using EEG data collected with the Emotiv EPOC X headset. Seven participants underwent a body awareness training protocol integrating mindfulness and physical exercises to improve MI performance. Machine learning techniques were applied to extract discriminative features from the EEG signals.

RESULTS: Post-training assessments indicated modest improvements in participants' MI proficiency. However, classification performance was limited due to inter- and intra-subject signal variability and the technical constraints of the consumer-grade EEG hardware.

DISCUSSION: These findings highlight the value of combining user training with MI-based BCIs and the need to optimize signal quality for reliable performance. The results support the feasibility of scalable, multiclass MI paradigms in low-cost, user-centered neurotechnology applications, while pointing to critical areas for future system enhancement.},
}

@article {pmid40873632,
year = {2025},
author = {Ye, Y and Tian, Y and Liu, H and Liu, J and Zhou, C and Xu, C and Zhou, T and Nie, Y and Wu, Y and Qin, L and Zhou, Z and Wei, X and Zhao, J and Wang, Z and Li, M and Tao, TH and Sun, L},
title = {High-Precision, Low-Threshold Neuromodulation With Ultraflexible Electrode Arrays for Brain-to-Brain Interfaces.},
journal = {Exploration (Beijing, China)},
volume = {5},
number = {4},
pages = {e70040},
pmid = {40873632},
issn = {2766-2098},
abstract = {Neuromodulation is crucial for advancing neuroscience and treating neurological disorders. However, traditional methods using rigid electrodes have been limited by large stimulating currents, low precision, and the risk of tissue damage. In this work, we developed a biocompatible ultraflexible electrode array that allows for both neural recording of spike firings and low-threshold, high-precision stimulation for neuromodulation. Specifically, mouse turning behavior can be effectively induced with approximately five microamperes of stimulating current, which is significantly lower than that required by conventional rigid electrodes. The array's densely packed microelectrodes enable highly selective stimulation, allowing precise targeting of specific brain areas critical for turning behavior. This low-current, targeted stimulation approach helps maintain the health of both neurons and electrodes, as evidenced by stable neural recordings after extended stimulations. Systematic validations have confirmed the durability and biocompatibility of the electrodes. Moreover, we extended the flexible electrode array to a brain-to-brain interface system that allows human brain signals to directly control mouse behavior. Using advanced decoding methods, a single individual can issue eight commands to simultaneously control the behaviors of two mice. This study underscores the effectiveness of the flexible electrode array in neuromodulation, opening new avenues for interspecies communication and potential neuromodulation applications.},
}

@article {pmid40872076,
year = {2025},
author = {Zhang, M and Qian, B and Gao, J and Zhao, S and Cui, Y and Luo, Z and Shi, K and Yin, E},
title = {Recent Advances in Portable Dry Electrode EEG: Architecture and Applications in Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
doi = {10.3390/s25165215},
pmid = {40872076},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; Humans ; Electrodes ; *Brain/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {As brain-computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems.},
}

*Brain-Computer Interfaces
*Electroencephalography/instrumentation/methods
Humans
Electrodes
*Brain/physiology
Algorithms
Signal Processing, Computer-Assisted

@article {pmid40872049,
year = {2025},
author = {Khuntia, PK and Bhide, PS and Manivannan, PV},
title = {Preliminary Analysis and Proof-of-Concept Validation of a Neuronally Controlled Visual Assistive Device Integrating Computer Vision with EEG-Based Binary Control.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
doi = {10.3390/s25165187},
pmid = {40872049},
issn = {1424-8220},
support = {SB22230362MEPMRF000758//Prime Minister's Research Fellowship by The Government of India/ ; },
mesh = {*Electroencephalography/methods ; Humans ; Algorithms ; *Self-Help Devices ; Signal Processing, Computer-Assisted ; Robotics ; },
abstract = {Contemporary visual assistive devices often lack immersive user experience due to passive control systems. This study introduces a neuronally controlled visual assistive device (NCVAD) that aims to assist visually impaired users in performing reach tasks with active, intuitive control. The developed NCVAD integrates computer vision, electroencephalogram (EEG) signal processing, and robotic manipulation to facilitate object detection, selection, and assistive guidance. The monocular vision-based subsystem implements the YOLOv8n algorithm to detect objects of daily use. Then, audio prompting conveys the detected objects' information to the user, who selects their targeted object using a voluntary trigger decoded through real-time EEG classification. The target's physical coordinates are extracted using ArUco markers, and a gradient descent-based path optimization algorithm (POA) guides a 3-DoF robotic arm to reach the target. The classification algorithm achieves over 85% precision and recall in decoding EEG data, even with coexisting physiological artifacts. Similarly, the POA achieves approximately 650 ms of actuation time with a 0.001 learning rate and 0.1 cm[2] error threshold settings. In conclusion, the study also validates the preliminary analysis results on a working physical model and benchmarks the robotic arm's performance against human users, establishing the proof-of-concept for future assistive technologies integrating EEG and computer vision paradigms.},
}

*Electroencephalography/methods
Humans
Algorithms
*Self-Help Devices
Signal Processing, Computer-Assisted
Robotics

@article {pmid40871906,
year = {2025},
author = {Isaev, M and Bobrov, P and Mokienko, O and Fedotova, I and Lyukmanov, R and Ikonnikova, E and Cherkasova, A and Suponeva, N and Piradov, M and Ustinova, K},
title = {Hemodynamic Response Asymmetry During Motor Imagery in Stroke Patients: A Novel NIRS-BCI Assessment Approach.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
doi = {10.3390/s25165040},
pmid = {40871906},
issn = {1424-8220},
support = {No: 1021062411635-8-3.1.4 and Registration No: 122041800162-9//Ministry of Science and Higher Education of the Russian Federation/ ; },
mesh = {Humans ; Spectroscopy, Near-Infrared/methods ; *Brain-Computer Interfaces ; Male ; Female ; Middle Aged ; *Stroke/physiopathology ; Stroke Rehabilitation/methods ; *Hemodynamics/physiology ; Aged ; Hemoglobins/metabolism ; Adult ; },
abstract = {Understanding patterns of interhemispheric asymmetry is crucial for monitoring neuroplastic changes during post-stroke motor rehabilitation. However, conventional laterality indices often pose computational challenges when applied to functional near-infrared spectroscopy (fNIRS) data due to the bidirectional hemodynamic responses. In this study, we analyze fNIRS recordings from 15 post-stroke patients undergoing motor imagery brain-computer interface training across multiple sessions. We compare traditional laterality coefficients with a novel task response asymmetry coefficient (TRAC), which quantifies differential hemispheric involvement during motor imagery tasks. Both indices are calculated for oxygenated and deoxygenated hemoglobin responses using general linear model coefficients, and their day-to-day dynamics are assessed with linear regression. The proposed TRAC demonstrates greater sensitivity than conventional measures, revealing significantly higher oxygenated hemoglobin TRAC values (0.18 ± 0.19 vs. -0.05 ± 0.20, p < 0.05) and lower deoxygenated hemoglobin TRAC values (-0.15 ± 0.27 vs. 0.04 ± 0.23, p < 0.05) in lesioned compared to intact hemispheres. Among patients who exhibit substantial motor recovery, distinct daily TRAC dynamics were observed, with statistically significant temporal trends. Overall, the novel TRAC metric offers enhanced discrimination of interhemispheric asymmetry patterns and captures temporal neuroplastic changes not detected by conventional indices, providing a more sensitive biomarker for tracking rehabilitation progress in post-stroke brain-computer interface applications.},
}

Humans
Spectroscopy, Near-Infrared/methods
*Brain-Computer Interfaces
Male
Female
Middle Aged
*Stroke/physiopathology
Stroke Rehabilitation/methods
*Hemodynamics/physiology
Aged
Hemoglobins/metabolism
Adult

@article {pmid40871892,
year = {2025},
author = {Moreno-Castelblanco, SR and Vélez-Guerrero, MA and Callejas-Cuervo, M},
title = {Artificial Intelligence Approaches for EEG Signal Acquisition and Processing in Lower-Limb Motor Imagery: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
doi = {10.3390/s25165030},
pmid = {40871892},
issn = {1424-8220},
support = {SGI 3904//Universidad Pedagógica y Tecnológica de Colombia/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Brain-Computer Interfaces ; *Lower Extremity/physiology ; *Artificial Intelligence ; *Signal Processing, Computer-Assisted ; Movement/physiology ; Algorithms ; *Imagination/physiology ; },
abstract = {BACKGROUND: Motor imagery (MI) is defined as the cognitive ability to simulate motor movements while suppressing muscular activity. The electroencephalographic (EEG) signals associated with lower limb MI have become essential in brain-computer interface (BCI) research aimed at assisting individuals with motor disabilities.

OBJECTIVE: This systematic review aims to evaluate methodologies for acquiring and processing EEG signals within brain-computer interface (BCI) applications to accurately identify lower limb MI.

METHODS: A systematic search in Scopus and IEEE Xplore identified 287 records on EEG-based lower-limb MI using artificial intelligence. Following PRISMA guidelines (non-registered), 35 studies met the inclusion criteria after screening and full-text review.

RESULTS: Among the selected studies, 85% applied machine or deep learning classifiers such as SVM, CNN, and LSTM, while 65% incorporated multimodal fusion strategies, and 50% implemented decomposition algorithms. These methods improved classification accuracy, signal interpretability, and real-time application potential. Nonetheless, methodological variability and a lack of standardization persist across studies, posing barriers to clinical implementation.

CONCLUSIONS: AI-based EEG analysis effectively decodes lower-limb motor imagery. Future efforts should focus on harmonizing methods, standardizing datasets, and developing portable systems to improve neurorehabilitation outcomes. This review provides a foundation for advancing MI-based BCIs.},
}

Humans
*Electroencephalography/methods
Brain-Computer Interfaces
*Lower Extremity/physiology
*Artificial Intelligence
*Signal Processing, Computer-Assisted
Movement/physiology
Algorithms
*Imagination/physiology

@article {pmid40871810,
year = {2025},
author = {Alahaideb, L and Al-Nafjan, A and Aljumah, H and Aldayel, M},
title = {Brain-Computer Interface for EEG-Based Authentication: Advancements and Practical Implications.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {16},
pages = {},
doi = {10.3390/s25164946},
pmid = {40871810},
issn = {1424-8220},
support = {(13461-imamu-2023-IMIU-R-3-1-HW-).//The Research, Development, and Innovation Authority (RDIA) - Kingdom of Saudi Arabia/ ; },
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Computer Security ; },
abstract = {Authentication is a critical component of digital security, and traditional methods often encounter significant vulnerabilities and limitations. This study addresses the emerging field of EEG-based authentication systems, highlighting their theoretical advancements and practical applicability. We conducted a systematic review of the existing literature, followed by an experimental evaluation to assess the feasibility, limitations, and scalability of these systems in real-world scenarios. Data were collected from nine subjects using various approaches. Our results indicate that the CNN model achieved the highest accuracy of 99%, while Random Forest (RF) and Gradient Boosting (GB) classifiers also demonstrated strong performance with 94% and 93%, respectively. In contrast, classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) displayed significantly lower effectiveness, underscoring their limitations in capturing the complexities of EEG data. The findings suggest that EEG-based authentication systems have significant potential to enhance security measures, offering a promising alternative to traditional methods and paving the way for more robust and user-friendly authentication solutions.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
Support Vector Machine
Algorithms
Neural Networks, Computer
Computer Security

@article {pmid40868398,
year = {2025},
author = {Kumar, R and Sporn, K and Kaur, H and Khanna, A and Paladugu, P and Zaman, N and Tavakkoli, A},
title = {Current Mechanobiological Pathways and Therapies Driving Spinal Health.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {8},
pages = {},
doi = {10.3390/bioengineering12080886},
pmid = {40868398},
issn = {2306-5354},
abstract = {Spinal health depends on the dynamic interplay between mechanical forces, biochemical signaling, and cellular behavior. This review explores how key molecular pathways, including integrin, yeas-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ), Piezo, and Wingless/Integrated (Wnt) with β-catenin, actively shape the structural and functional integrity of spinal tissues. These signaling mechanisms respond to physical cues and interact with inflammatory mediators such as interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α), driving changes that lead to disc degeneration, vertebral fractures, spinal cord injury, and ligament failure. New research is emerging that shows scaffold designs that can directly harness these pathways. Further, new stem cell-based therapies have been shown to promote disc regeneration through targeted differentiation and paracrine signaling. Interestingly, many novel bone and ligament scaffolds are modulating anti-inflammatory signals to enhance tissue repair and integration, as well as prevent scaffold degradation. Neural scaffolds are also arising. These mimic spinal biomechanics and activate Piezo signaling to guide axonal growth and restore motor function. Scientists have begun combining these biological platforms with brain-computer interface technology to restore movement and sensory feedback in patients with severe spinal damage. Although this technology is not fully clinically ready, this field is advancing rapidly. As implantable technology can now mimic physiological processes, molecular signaling, biomechanical design, and neurotechnology opens new possibilities for restoring spinal function and improving the quality of life for individuals with spinal disorders.},
}

@article {pmid40868333,
year = {2025},
author = {Tonin, A and Semprini, M and Kiper, P and Mantini, D},
title = {Brain-Computer Interfaces for Stroke Motor Rehabilitation.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {12},
number = {8},
pages = {},
doi = {10.3390/bioengineering12080820},
pmid = {40868333},
issn = {2306-5354},
abstract = {Brain-computer interface (BCI) technology holds promise for improving motor rehabilitation in stroke patients. This review explores the immediate and long-term effects of BCI training, shedding light on the potential benefits and challenges. Clinical studies have demonstrated that BCIs yield significant immediate improvements in motor functions following stroke. Patients can engage in BCI training safely, making it a viable option for rehabilitation. Evidence from single-group studies consistently supports the effectiveness of BCIs in enhancing patients' performance. Despite these promising findings, the evidence regarding long-term effects remains less robust. Further studies are needed to determine whether BCI-induced changes are permanent or only last for short durations. While evaluating the outcomes of BCI, one must consider that different BCI training protocols may influence functional recovery. The characteristics of some of the paradigms that we discuss are motor imagery-based BCIs, movement-attempt-based BCIs, and brain-rhythm-based BCIs. Finally, we examine studies suggesting that integrating BCIs with other devices, such as those used for functional electrical stimulation, has the potential to enhance recovery outcomes. We conclude that, while BCIs offer immediate benefits for stroke rehabilitation, addressing long-term effects and optimizing clinical implementation remain critical areas for further investigation.},
}

@article {pmid40867492,
year = {2025},
author = {Guan, S and Meng, F and Wu, C},
title = {Authoritative Filial Piety Rather than Reciprocal Filial Piety Mediated the Relationship Between Parental Support, Career Decision Self-Efficacy, and Discrepancies Between Individual-Set and Parent-Set Career Goals.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/bs15081135},
pmid = {40867492},
issn = {2076-328X},
support = {2023DSYL022//The Supervisor Guidance Program of Shanghai International Studies University/ ; 22YJC880018//General Project of Humanities and Social Sciences of the Ministry of Education 'Research on the Internationalization Path and Strategy of Vocational Education in China from the Perspective of Regional and Country Analysis'/ ; 23ZD010//Fundamental Research Funds for the Central Universities/ ; 2020EYY004//Shanghai Philosophy and Social Science Planning Youth Project/ ; 20CG40//Shanghai Chenguang Talent Program/ ; 2020114052//The Innovative Research Team of Shanghai International Studies University/ ; 2022KFKT009//Open project of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; B202205//Open project of Key Laboratory of Multilingual Education with AI/ ; },
abstract = {Although a wealth of research has examined the predictors influencing the discrepancies between individual-set and parent-set career goals (DBIPCG), investigations grounded in collectivist cultural perspectives remain relatively scarce. Within collectivist societies, filial piety holds profound cultural significance. Drawing on a dual filial piety framework encompassing reciprocal filial piety (RFP) and authoritative filial piety (AFP), this study aims to explore the interconnections among parental support, self-efficacy in career decision-making, dual filial piety orientations, and DBIPCG. The results indicated that parental support was negatively associated with DBIPCG. By contrast, self-efficacy in career decision-making did not predict DBIPCG directly. Instead, self-efficacy indirectly influenced DBIPCG, an effect mediated specifically by AFP rather than RFP, Furthermore, AFP was found to mediate the link between parental support and DBIPCG. These findings underscore the role of parental support in minimizing differences in career goal formation between generations and highlight the potentially adverse implications of AFP in exacerbating such discrepancies.},
}

@article {pmid40867216,
year = {2025},
author = {Zhao, Y and Cao, L and Ji, Y and Wang, B and Wu, W},
title = {Interpretable EEG Emotion Classification via CNN Model and Gradient-Weighted Class Activation Mapping.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/brainsci15080886},
pmid = {40867216},
issn = {2076-3425},
support = {4244100//Beijing Natural Science Foundation/ ; },
abstract = {Background/Objectives: Electroencephalography (EEG)-based emotion recognition plays an important role in affective computing and brain-computer interface applications. However, existing methods often face the challenge of achieving high classification accuracy while maintaining physiological interpretability. Methods: In this study, we propose a convolutional neural network (CNN) model with a simple architecture for EEG-based emotion classification. The model achieves classification accuracies of 95.21% for low/high arousal, 94.59% for low/high valence, and 93.01% for quaternary classification tasks on the DEAP dataset. To further improve model interpretability and support practical applications, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to identify the EEG electrode regions that contribute most to the classification results. Results: The visualization reveals that electrodes located in the right prefrontal cortex and left parietal lobe are the most influential, which is consistent with findings from emotional lateralization theory. Conclusions: This provides a physiological basis for optimizing electrode placement in wearable EEG-based emotion recognition systems. The proposed method combines high classification performance with interpretability and provides guidance for the design of efficient and portable affective computing systems.},
}

@article {pmid40867214,
year = {2025},
author = {Han, Q and Sun, Y and Ye, H and Song, Z and Zhao, J and Shi, L and Kuang, Z},
title = {GAH-TNet: A Graph Attention-Based Hierarchical Temporal Network for EEG Motor Imagery Decoding.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/brainsci15080883},
pmid = {40867214},
issn = {2076-3425},
support = {YDZJ202201ZYTS684//Jilin Province Science and Technology Department/ ; },
abstract = {BACKGROUND: Brain-computer interfaces (BCIs) based on motor imagery (MI) offer promising solutions for motor rehabilitation and communication. However, electroencephalography (EEG) signals are often characterized by low signal-to-noise ratios, strong non-stationarity, and significant inter-subject variability, which pose significant challenges for accurate decoding. Existing methods often struggle to simultaneously model the spatial interactions between EEG channels, the local fine-grained features within signals, and global semantic patterns.

METHODS: To address this, we propose the graph attention-based hierarchical temporal network (GAH-TNet), which integrates spatial graph attention modeling with hierarchical temporal feature encoding. Specifically, we design the graph attention temporal encoding block (GATE). The graph attention mechanism is used to model spatial dependencies between EEG channels and encode short-term temporal dynamic features. Subsequently, a hierarchical attention-guided deep temporal feature encoding block (HADTE) is introduced, which extracts local fine-grained and global long-term dependency features through two-stage attention and temporal convolution. Finally, a fully connected classifier is used to obtain the classification results. The proposed model is evaluated on two publicly available MI-EEG datasets.

RESULTS: Our method outperforms multiple existing state-of-the-art methods in classification accuracy. On the BCI IV 2a dataset, the average classification accuracy reaches 86.84%, and on BCI IV 2b, it reaches 89.15%. Ablation experiments validate the complementary roles of GATE and HADTE in modeling. Additionally, the model exhibits good generalization ability across subjects.

CONCLUSIONS: This framework effectively captures the spatio-temporal dynamic characteristics and topological structure of MI-EEG signals. This hierarchical and interpretable framework provides a new approach for improving decoding performance in EEG motor imagery tasks.},
}

@article {pmid40867208,
year = {2025},
author = {Lian, X and Liu, C and Gao, C and Deng, Z and Guan, W and Gong, Y},
title = {A Multi-Branch Network for Integrating Spatial, Spectral, and Temporal Features in Motor Imagery EEG Classification.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/brainsci15080877},
pmid = {40867208},
issn = {2076-3425},
support = {62173007//National Natural Science Foundation of China/ ; },
abstract = {Background: Efficient decoding of motor imagery (MI) electroencephalogram (EEG) signals is essential for the precise control and practical deployment of brain-computer interface (BCI) systems. Owing to the complex nonlinear characteristics of EEG signals across spatial, spectral, and temporal dimensions, efficiently extracting multidimensional discriminative features remains a key challenge to improving MI-EEG decoding performance. Methods: To address the challenge of capturing complex spatial, spectral, and temporal features in MI-EEG signals, this study proposes a multi-branch deep neural network, which jointly models these dimensions to enhance classification performance. The network takes as inputs both a three-dimensional power spectral density tensor and two-dimensional time-domain EEG signals and incorporates four complementary feature extraction branches to capture spatial, spectral, spatial-spectral joint, and temporal dynamic features, thereby enabling unified multidimensional modeling. The model was comprehensively evaluated on two widely used public MI-EEG datasets: EEG Motor Movement/Imagery Database (EEGMMIDB) and BCI Competition IV Dataset 2a (BCIIV2A). To further assess interpretability, gradient-weighted class activation mapping (Grad-CAM) was employed to visualize the spatial and spectral features prioritized by the model. Results: On the EEGMMIDB dataset, it achieved an average classification accuracy of 86.34% and a kappa coefficient of 0.829 in the five-class task. On the BCIIV2A dataset, it reached an accuracy of 83.43% and a kappa coefficient of 0.779 in the four-class task. Conclusions: These results demonstrate that the network outperforms existing state-of-the-art methods in classification performance. Furthermore, Grad-CAM visualizations identified the key spatial channels and frequency bands attended to by the model, supporting its neurophysiological interpretability.},
}

@article {pmid40867186,
year = {2025},
author = {Vanutelli, ME and Banzi, A and Cicirello, M and Folgieri, R and Lucchiari, C},
title = {Predicting State Anxiety Level Change Using EEG Parameters: A Pilot Study in Two Museum Settings.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/brainsci15080855},
pmid = {40867186},
issn = {2076-3425},
abstract = {Background: Museums are increasingly being recognized not only as cultural institutions but also as potential resources for enhancing psychological well-being. Prior research has shown that museum visits can reduce stress and anxiety, yet there is a pressing need for evidence-based interventions supported by neurophysiological data. While neuroscientific studies suggest a combined role of emotional and cognitive mechanisms in aesthetic experiences, less is known about the neural predictors of individual responsiveness to such interventions. Methods: This study was conducted in two Milan-based museums and included an initial profiling phase (sociodemographic information, trait anxiety, perceived stress, museum experience), followed by pre- and post-visit assessments of state anxiety and mood. Electrocortical activity was recorded via a portable brain-computer interface (BCI), focusing on the theta/beta ratio (TBR) as a marker of cortical-subcortical integration. Results: Museum visits were associated with significant improvements in mood (M = 1.17; p < 0.001) and reductions in state anxiety (M = -6.36; p < 0.001) in both arts and science museums. The baseline TBR predicted the magnitude of state anxiety change, alongside individual differences in trait anxiety and perceived stress. Conclusions: These findings support the idea that aesthetic experiences in museums engage both emotional and cognitive systems, and that resting state neurophysiological markers can help forecast individual responsiveness to well-being interventions. Such insights not only contribute to existing knowledge about the cognitive and emotional processes during aesthetic fruition, but could also guide future applications of personalized interventions in museum settings, further integrating cultural participation with mental health promotion.},
}

@article {pmid40867148,
year = {2025},
author = {Serna, B and Salazar, R and Alonso-Silverio, GA and Baltazar, R and Ventura-Molina, E and Alarcón-Paredes, A},
title = {Fear Detection Using Electroencephalogram and Artificial Intelligence: A Systematic Review.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/brainsci15080815},
pmid = {40867148},
issn = {2076-3425},
abstract = {Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string ("fear detection" AND "artificial intelligence" OR "machine learning" AND NOT "fnirs OR mri OR ct OR pet OR image"). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing.},
}

@article {pmid40867138,
year = {2025},
author = {Chen, X and Bao, X and Jitian, K and Li, R and Zhu, L and Kong, W},
title = {Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification.},
journal = {Brain sciences},
volume = {15},
number = {8},
pages = {},
doi = {10.3390/brainsci15080805},
pmid = {40867138},
issn = {2076-3425},
support = {62301196//National Science Foundation of China/ ; 2025C04001//"Pioneer" and "Leading ·Goose" R&D ·Program of Zhejiang/ ; LQ24F020035//Zhejiang Provincial Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human-computer interaction, and brain-computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions.

METHODS: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability.

RESULTS: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods.

CONCLUSIONS: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments.},
}

@article {pmid40864570,
year = {2025},
author = {Chen, R and Xie, C and Zhang, J and You, Q and Pan, J},
title = {A Progressive Multi-Domain Adaptation Network with Reinforced Self-Constructed Graphs for Cross-Subject EEG-Based Emotion and Consciousness Recognition.},
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.2025.3603190},
pmid = {40864570},
issn = {1558-0210},
abstract = {Electroencephalogram (EEG)-based emotion recognition is a vital component in brain-computer interface applications. However, it faces two significant challenges: (1) extracting domain-invariant features while effectively preserving emotion-related information, and (2) aligning the joint probability distributions of data across different individuals. To address these challenges, we propose a progressive multi-domain adaptation network with reinforced self-constructed graphs. Specifically, we introduce EEG-CutMix to construct unlabeled mixed-domain data, facilitating the transition between source and target domains. Additionally, a reinforced self-constructed graphs module is employed to extract domain-invariant features. Finally, a progressive multi-domain adaptation framework is constructed to smoothly align the data distributions across individuals. Experiments on cross-subject datasets demonstrate that our model achieves state-of-the-art performance on the SEED and SEED-IV datasets, with accuracies of 97.03% ± 1.65% and 88.18% ± 4.55%, respectively. Furthermore, tests on a self-recorded dataset, comprising ten healthy subjects and twelve patients with disorders of consciousness (DOC), show that our model achieves a mean accuracy of 86.65% ± 2.28% in healthy subjects. Notably, it successfully applies to DOC patients, with four subjects achieving emotion recognition accuracy exceeding 70%. These results validate the effectiveness of our model in EEG emotion recognition and highlight its potential for assessing consciousness levels in DOC patients. The source code for the proposed model is available at GitHub-seizeall/mycode.},
}