@article {pmid41684733,
year = {2026},
author = {Ritzmann, R and De Pauw, K and Wollesen, B},
title = {Editorial: Neuro-cognition in human movement: from fundamental experiments to bio-inspired innovation.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1625712},
pmid = {41684733},
issn = {1664-2295},
}

@article {pmid41682716,
year = {2026},
author = {Gouret, A and Delaux, A and Le Bars, S and Chokron, S},
title = {Rewiring Attention: Virtual Reality and Brain-Computer Interfaces in the Rehabilitation of Unilateral Spatial Neglect.},
journal = {Journal of clinical medicine},
volume = {15},
number = {3},
pages = {},
doi = {10.3390/jcm15031036},
pmid = {41682716},
issn = {2077-0383},
support = {CIFRE 2022/1439//Association Nationale de la Recherche et de la Technol-722 ogie/ ; },
abstract = {Unilateral spatial neglect (USN) is a complex cognitive syndrome frequently observed after stroke. Characterized by a failure to attend, respond and orient to stimuli on the side opposite the brain lesion, USN significantly impairs patients' functional independence and presents significant challenges for rehabilitation. Current rehabilitation strategies often fall short in addressing the heterogenous manifestations of USN across perceptual modalities due to limited ecological validity, patient engagement and adaptability to individual needs. Recent advances in neurotechnologies such as virtual reality (VR) and brain-computer interfaces (BCIs) offer promising avenues for overcoming these limitations. These tools enable top-down rehabilitation strategies that directly engage cognitive recovery mechanisms to promote neuroplasticity, and support adaptive interventions tailored to individual profiles. This narrative review explores recent developments and future prospects of VR and BCI technologies in the rehabilitation of USN, both individually and in combination. After outlining key features of USN to frame rehabilitation challenges, it examines VR, BCI, and their integrated applications in this context. While there is growing evidence supporting VR interventions efficacy in enhancing conventional strategies and alleviating USN symptoms, research on BCI applications in this context is still emerging. Nevertheless, insights from broader neurorehabilitation research suggest that combining VR and BCI holds significant promise for advancing cognitive rehabilitation and addressing USN-specific challenges. To illustrate the transformative value of advanced USN interventions, we present a concrete example of a VR-BCI integrated rehabilitation framework in the making, designed to provide a comprehensive and personalized therapeutic approach, bridging technological potential with clinical rehabilitation needs.},
}

@article {pmid41682533,
year = {2026},
author = {Wolfers, J and Hurst, W and Krampe, C},
title = {Integrating EEG Sensors with Virtual Reality to Support Students with ADHD.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {3},
pages = {},
doi = {10.3390/s26031017},
pmid = {41682533},
issn = {1424-8220},
mesh = {Humans ; *Attention Deficit Disorder with Hyperactivity/physiopathology ; *Electroencephalography/methods ; *Virtual Reality ; Male ; Students ; Female ; Brain-Computer Interfaces ; Adolescent ; Young Adult ; Attention/physiology ; Adult ; Brain/physiopathology ; },
abstract = {Students with attention deficit hyperactivity disorder (ADHD) face a continuous challenge with their attention span, putting them at a greater risk of academic or psychological difficulties compared to their peers. Innovative communication technologies are demonstrating potential to address these attention-span concerns. Virtual Reality (VR) is one such example, and has the potential to address attention-span difficulties among ADHD students. Accordingly, this study presents an EEG-based multimodal sensing pipeline as a methodological contribution, focusing on sensor-based data acquisition, signal processing, and neurophysiological interpretation to assess attention in VR-based environments, simulating a university supply chain educational topic. Thus, in this paper, a sequential exploratory approach investigated how 35 participants experienced an interactive VR-learning-driven supply chain game. A Brain-Computer Interaction (BCI) sensor generated insights by quantitatively analysing electroencephalogram (EEG) data that were processed through the proposed pipeline and integrated with subjective measures to validate participant's subjective feelings. These insights originated from questions during the experiment that followed the Spatial Presence and Technology Acceptance Model to form a multimodal assessment framework. Findings demonstrated that the experimental group experienced a higher improved attention, concentration, engagement, and focus levels compared to the control group. BCI results from the experimental group showed more dominant voltage potentials in the right frontal and prefrontal cortex of the brain in areas responsible for attention, memory, and decision-making. A high acceptance of the VR technology among neurodiverse students highlights the added benefits of multimodal learning assessment methods in an educational setting.},
}

Humans
*Attention Deficit Disorder with Hyperactivity/physiopathology
*Electroencephalography/methods
*Virtual Reality
Male
Students
Female
Brain-Computer Interfaces
Adolescent
Young Adult
Attention/physiology
Adult
Brain/physiopathology

@article {pmid41682467,
year = {2026},
author = {Hassanloo, M and Zareh, A and Özdemir, MK},
title = {High-Accuracy Detection of Odor Presence from Olfactory Bulb Local Field Potentials via Deep Neural Networks.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {3},
pages = {},
doi = {10.3390/s26030951},
pmid = {41682467},
issn = {1424-8220},
support = {123E520//Scientific and Technological Research Council of Turkey/ ; },
mesh = {*Odorants/analysis ; *Olfactory Bulb/physiology ; Animals ; Mice ; *Neural Networks, Computer ; Deep Learning ; Smell/physiology ; },
abstract = {Odor detection underpins food safety, environmental monitoring, medical diagnostics, and many more fields. Current artificial sensors developed for odor detection struggle with complex mixtures, while non-invasive recordings lack reliable single-trial fidelity. To develop a general system for odor detection, in this study we present preliminary work where we test two hypotheses: (i) that spectral features of local field potentials (LFPs) are sufficient for robust single-trial odor detection and (ii) that signals from the olfactory bulb alone are adequate. To test these hypotheses, we propose an ensemble of complementary one-dimensional convolutional networks (ResCNN and AttentionCNN) that decodes the presence of odor from multichannel olfactory bulb LFPs. Tested on 2349 trials from seven awake mice, our final ensemble model supports both hypotheses, achieving a mean accuracy of 86.2%, an F1-score of 85.3%, and an AUC of 0.942, substantially outperforming previous benchmarks. The t-SNE visualization confirms that our framework captures biologically significant signatures. These findings establish the feasibility of robust single-trial detection of odor presence from extracellular LFPs and demonstrate the potential of deep learning models to provide deeper understanding of olfactory representations.},
}

*Odorants/analysis
*Olfactory Bulb/physiology
Animals
Mice
*Neural Networks, Computer
Deep Learning
Smell/physiology

@article {pmid41682433,
year = {2026},
author = {Kołodziej, M and Majkowski, A and Wiszniewski, P},
title = {Improved SSVEP Classification Through EEG Artifact Reduction Using Auxiliary Sensors.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {3},
pages = {},
doi = {10.3390/s26030917},
pmid = {41682433},
issn = {1424-8220},
support = {Research was funded by Warsaw University of Technology within the Excellence Initiative: Research University (IDUB) program.//Warsaw University of Technology/ ; },
mesh = {Humans ; *Electroencephalography/methods ; Artifacts ; Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Male ; Adult ; Female ; Algorithms ; Signal Processing, Computer-Assisted ; Young Adult ; Brain/physiology ; Electrooculography ; },
abstract = {Steady-state visual evoked potentials (SSVEPs) are one of the key paradigms used in brain-computer interface (BCI) systems. Their performance, however, is substantially degraded by EEG artifacts of muscular, motion-related, and ocular origin. This issue is particularly pronounced in individuals exhibiting increased facial muscle tension or involuntary eye movements. The aim of this study was to develop and evaluate an EEG artifact reduction method based on auxiliary channels, including central (Cz), frontal (Fp1), electrooculographic (HEOG), and muscular electrodes (neck, cheek, jaw). Signals from these channels were used to model the physical sources of interference recorded concurrently with occipital brain activity (O1, O2, Oz). EEG signal cleaning was performed using linear regression in 1-s windows, followed by frequency-domain analysis to extract features related to stimulation frequencies and SSVEP classification using SVM and CNN algorithms. The experiment involved three visual stimulation frequencies (7, 8, and 9 Hz) generated by LEDs and the recording of controlled facial and jaw-related artifacts. Experiments conducted on 12 participants demonstrated a 9% increase in classification accuracy after artifact removal. Further analysis indicated that the Cz and jaw channels contributed most significantly to effective artifact suppression. The results confirm that the use of auxiliary channels substantially improves EEG signal quality and enhances the reliability of BCI systems under real-world conditions.},
}

Humans
*Electroencephalography/methods
Artifacts
Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
Male
Adult
Female
Algorithms
Signal Processing, Computer-Assisted
Young Adult
Brain/physiology
Electrooculography

@article {pmid41682282,
year = {2026},
author = {Angrisani, L and De Benedetto, E and D'Iorio, M and Duraccio, L and Lo Regio, F and Tedesco, A},
title = {Online Compensation of Systematic Effects in Stimuli Generation for XR-Based SSVEP BCIs.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {3},
pages = {},
doi = {10.3390/s26030766},
pmid = {41682282},
issn = {1424-8220},
support = {E63C22002040007//Ministero dell'università e della ricerca/ ; E63C22002130007//Ministero dell'università e della ricerca/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Evoked Potentials, Visual/physiology ; Electroencephalography/methods ; Adult ; Male ; Female ; Algorithms ; Young Adult ; },
abstract = {Background: Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) and Extended Reality (XR) offer promising solutions for highly wearable applications, but their classification performance can be affected by systematic effects in stimulus presentation. Novelty: This study introduces a novel online compensation method to compensate for systematic effects in the Refresh Rate (RR) of XR displays, enhancing SSVEP classification without requiring additional training or invasive measurements. Methods: A non-invasive monitoring module was incorporated into the developed BCI pipeline to measure frame rate variations in the XR display, allowing deviations between nominal RR and measured values to be automatically detected and compensated for. Classification performance was evaluated using Filter Bank Canonical Correlation Analysis (FBCCA). Statistical significance was assessed using Student's t-test. Materials: Two datasets were used: a dataset based on Moverio BT-350, including 9 subjects, and a dataset based on HoloLens 2, including 30 subjects, all collected by the authors. Results: The proposed compensation method led to significant improvements in SSVEP classification accuracy, proportional to the magnitude of fps deviations. In some cases, classification accuracy increased by up to 300% relative to its original value. Statistical analyses confirmed the reliability of the results across subjects and datasets. Conclusions: These findings show that the proposed method effectively enhances SSVEP-based BCIs in XR environments and provides a robust foundation for practical applications requiring high reliability.},
}

Humans
*Brain-Computer Interfaces
*Evoked Potentials, Visual/physiology
Electroencephalography/methods
Adult
Male
Female
Algorithms
Young Adult

@article {pmid41678839,
year = {2026},
author = {Abreu, EA and Giarusso de Vazquez, PF and Castellano, G},
title = {Decoding Inner Speech with functional connectivity.},
journal = {Biomedical physics & engineering express},
volume = {},
number = {},
pages = {},
doi = {10.1088/2057-1976/ae451b},
pmid = {41678839},
issn = {2057-1976},
abstract = {Inner-Speech (IS) based Brain-Computer Interfaces (BCIs) offer potential communication solutions for individuals with disabilities by decoding brain signals generated during speech imagination. While most IS-BCI systems rely on time-frequency EEG features, this study investigates functional connectivity-specifically, motif synchronization (MS)-to determine whether interactions between brain regions improve the discrimination of imagined words. Methods: We analyzed EEG data from the "Thinking Out Loud" dataset by Results: The model achieved an average classification accuracy of 45.8%, outperforming two of three prior studies using the same dataset while offering greater generalizability than the third (which reported higher accuracy). Conclusions: Functional connectivity features, particularly motif synchronization, show promise in IS-BCI applications by leveraging cross-regional brain interactions. This approach advances neurophysiological signal analysis and can enhance assistive technology and cognitive research. However, larger datasets are required to improve the robustness and validate scalability. .},
}

@article {pmid41677796,
year = {2026},
author = {Zhai, X and Hao, Z and Wang, X and Li, C and Cao, Y and Peng, B and Qi, X and Ni, X and Xie, R and Dou, W and Pan, Y},
title = {Effect of Brain-Computer Interface-Controlled Ankle Robot Training on Post-Stroke Motor Rehabilitation and Resting QEEG Neuroplasticity: A Randomized Controlled Trial.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683251412286},
doi = {10.1177/15459683251412286},
pmid = {41677796},
issn = {1552-6844},
abstract = {BACKGROUND: Persistent post-stroke ankle impairment hinders functional recovery. Brain-computer interface (BCI)-controlled ankle robot show rehabilitation potential, but their efficacy and underlying neuroplasticity remain unclear.

OBJECTIVE: To assess BCI-controlled ankle robot training on post-stroke lower-limb motor recovery and neuroplasticity using quantitative EEG (qEEG).

METHODS: Thirty-two stroke patients were randomized to BCI (n = 16, 40-minute BCI-robot training) or control (n = 16, 40-minute ankle-robot training) groups, receiving 5 sessions/week for 2 weeks. Outcomes included Fugl-Meyer Assessment-Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Functional Ambulatory Category (FAC), Modified Ashworth Scale (MAS), active range of motion (AROM), and muscle strength. QEEG assessed the relative power of the delta (rδ), theta (rθ), alpha (rα), beta (rβ) bands, spectral power ratios, pairwise-derived Brain Symmetry Index (pdBSI), and functional connectivity.

RESULTS: Both groups showed significant within-group improvements in dorsiflexion AROM, dorsiflexor strength, FMA-LE, BBS, and FAC (P < .05). The BCI group demonstrated significantly greater FMA-LE improvement than controls (∆FMA-LE, P = .007) and reduced calf spasticity (MAS; P = .038). QEEG analysis in the BCI group revealed decreased rδ (P = .005), increased rα (P = .017), reduced DAR and DTABR (P < .05), reduced interhemispheric asymmetry (pdBSI-δ; P = .018), and enhanced Cz-parietal connectivity in α and β bands (P < .05).

CONCLUSION: BCI-controlled ankle robot training significantly improved lower-limb motor function and reduced spasticity post-stroke. Associated neurophysiological changes, characterized by reduced slow-wave power and asymmetry, increased alpha power, and functional connectivity, indicated beneficial neuroplastic reorganization.Clinical trial registration number: China Clinical Trail Registry (ChiCTR2300074381; URL: http://www.chictr.org.cn).},
}

@article {pmid41676631,
year = {2026},
author = {Blumenthal, GH and Dekleva, BM and Gontier, C and Gonzalez, IC and Gonzalez-Martinez, JA and Yu, BM and Batista, AP and Sobinov, AR and Miller, LE and Gaunt, RA and Boninger, ML and Chase, SM and Collinger, JL},
title = {Distinct neural modes carry information about grasp force and phase in the sensorimotor cortex.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.02.01.702680},
pmid = {41676631},
issn = {2692-8205},
abstract = {UNLABELLED: Humans perform a variety of complex hand movements to manipulate objects, requiring precise control of changing forces. Understanding the role of sensorimotor cortex and the cortical dynamics underlying these actions is crucial for developing interventions that restore dexterous hand function after injury or disease. In this study, two individuals with tetraplegia resulting from cervical spinal cord injury attempted a series of isometric grasps. Neural activity was recorded from the motor and somatosensory cortices using intracortical microelectrode arrays while participants attempted to exert a static force or to ramp force up and down. Despite their inability to execute movement, and with limited afferent input, the spiking activity in motor and somatosensory cortex was modulated with the task. Within the neural response we identified independent neural modes - distinct patterns of population-level neural activity - that were informative about both the timing and magnitude of the force. Moreover, distinct neural modes were observed during static and dynamic grasping conditions, suggesting independent control schemes for maintaining and changing forces. These modes were related to phases of the task, including the onset, offset, holding periods, as well as phases of increasing and decreasing force. These results will inform the design of intracortical brain-computer interface (iBCI) systems that can leverage these naturally occurring patterns of grasp and force control to restore dexterous hand function.

SIGNIFICANCE STATEMENT: Restoring dexterous hand function after injury remains a major challenge, partly due to an incomplete understanding of the cortical dynamics underlying grasping and force control. In this study, we investigated neural activity within the motor and somatosensory cortices of individuals with tetraplegia attempting to perform grasps to different target forces with varying temporal profiles. We identified distinct neural modes modulated during specific phases of grasp that encode force information throughout the task. These findings suggest that brain-computer interfaces could leverage these native neural modes to restore grasping and force modulation.},
}

@article {pmid41676620,
year = {2026},
author = {Muralidharan, S and Leng, C and Orts, L and Trepka, E and Zhu, S and Panichello, M and Jonikaitis, D and Pennington, J and Pachitariu, M and Moore, T},
title = {A System for Live Sorting of Neuronal Spiking Activity from Large-scale Recordings.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2025.12.29.696938},
pmid = {41676620},
issn = {2692-8205},
abstract = {Online monitoring and quantification of neural signals has tremendous value both for neurofeedback experiments and for brain-computer interfaces. Unfortunately, established methods of online monitoring primarily involve the use of thresholded neural activity rather than sorted single-neuron spikes. The recent introduction of large-scale, high-density electrophysiology has enabled the recording of activity from hundreds of neurons simultaneously in both model organisms and human participants. This development highlights the need for a robust and easily implementable system for sorting spikes during data collection for live analyses of neuronal signals. Here, we describe a system for live sorting of neuronal activity (LSS) based on the widely used Kilosort platform. The LSS workflow utilizes an initial period of recorded neural data to identify waveform templates using Kilosort. LSS then interfaces with the SpikeGLX API to retrieve small batches (e.g. 50 ms) of data and for processing online. We measured the similarity of single-neuron activity sorted live by LSS to that sorted offline in neurophysiological recordings from macaque visual cortex using Neuropixels probes. We show that LSS closely replicates the post-stimulus time histograms and visual response tuning curves of single-neurons obtained using offline sorting. Furthermore, we show that decoding neural signals online with LSS consistently outperforms online decoding of thresholded activity, and that LSS can achieve the same performance as that obtained with offline sorting.},
}

@article {pmid41675914,
year = {2026},
author = {Qibin, Z and Lin, L and Yibiao, C and Peng, L and Huiqing, W and Daoqing, S and Lianghong, Y},
title = {RNA networks of lysosomal-related biomarkers in Parkinson's disease and their correlations with freezing of gait-associated genes.},
journal = {Frontiers in genetics},
volume = {17},
number = {},
pages = {1632163},
pmid = {41675914},
issn = {1664-8021},
abstract = {BACKGROUND: Parkinson's disease (PD) is influenced by various factors, with lysosome function playing a critical role. However, the specific involvement of lysosome-related genes (LRGs) in PD remains unclear.

OBJECTIVE: This study aims to identify biomarkers specific to PD that exhibit robust disease prediction capabilities.

METHODS: Datasets for patients with PD, LRGs, and inflammation-related genes (IRGs) were retrieved from online databases. miRNAs and mRNAs within key modules were selected through Weighted Gene Co-expression Network Analysis (WGCNA), revealing strong associations with PD. A miRNA-mRNA network was constructed based on highly correlated PD-related LRGs (PD-LRGs) and miRNAs within these modules. Candidate genes were identified by intersecting target genes, differentially expressed genes (DEGs), PD-LRGs, and module-associated mRNAs. Machine learning and expression validation were employed to confirm these biomarkers. A nomogram was established, and its diagnostic performance was evaluated using a confusion matrix. Drug predictions were conducted based on these biomarkers. Spearman's correlation analyses were performed to assess the relationship between IRGs, freezing of gait (FOG)-related genes, and biomarkers. Molecular regulatory networks were constructed using datasets and online resources. Finally, clinical samples were collected for quantitative PCR (qPCR) validation of biomarker expression.

RESULTS: Key modules related to PD were identified, comprising 190 miRNAs and 7,633 mRNAs. A miRNA-mRNA network was constructed based on 55 PD-LRGs and 181 miRNAs, resulting in the identification of 26 candidate genes strongly linked to lysosomal function. FGD4 and MAN2B1 were selected as biomarkers, and a gene expression-based risk prediction table was created. These biomarkers were significantly correlated with IRGs and several FOG-related genes. Gene localization analysis revealed that FGD4 and LRRK2, both critical to the FOG pathway, are located on chromosome 12. Drug prediction revealed that Tetrachlorodibenzodioxin and bisphenol A target both FGD4 and MAN2B1. qPCR analysis confirmed that FGD4 and MAN2B1 expression levels were significantly higher in patients with PD compared to healthy controls (p < 0.05).

CONCLUSION: FGD4 and MAN2B1 act as lysosomal biomarkers associated with PD and exhibit strong correlations with genes involved in PD-related freezing of gait. This study offers novel insights into PD diagnosis.},
}

@article {pmid41674222,
year = {2026},
author = {Ye, L and Fu, Y and Yang, X and Ning, H and Dong, Y and Zhao, Y and Zhang, L and Li, Y and Zhao, B and Guo, X and Gao, Y and Sun, K and Wang, K and Wang, J and Geng, H and Li, J and Ma, H and He, D},
title = {Nanoconfined Water Manipulated Selective Proton Storage in Layered Tungsten Oxides for Versatile Supercapacitor Diodes.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.6c00028},
pmid = {41674222},
issn = {1936-086X},
abstract = {Supercapacitor diode (CAPode) is an emerging type of electrochemical logic device that integrates ions and electrons as the coinformation carriers, thus being a promising building block for constructing new-type iontronic circuits and achieving seamless brain-computer interaction. However, the lack of understanding on its basic process, i.e., nanoconfined ion transport, greatly blocks the further enhancement of its ion rectification capability and ion transport kinetics. Herein, on the basis of in-depth analysis of the host-guest interactions in the nanoconfined space, a nanoconfined water mediated strategy is proposed to manipulate the ion transport behaviors in typical layered materials, i.e., tungsten oxides (WO3·nH2O, n = 0, 1, 2). The results reveal that WO3·H2O presents an optimal ion rectification capability and superior ion transport kinetics, much outperforming those of WO3·2H2O or WO3 with more or less structural water. Consequently, the WO3·H2O-based CAPode delivers a record-high rectification ratio of 253, an ultrahigh response frequency of 549 Hz, and an excellent cycling stability of up to 5000 cycles, enabling it to handle various complex ion/electron-coupling logic operations. More attractively, WO3·H2O is demonstrated to possess superior biocompatibility, endowing the as-built CAPode with great potential in the cutting-edge field of brain-computer interactions.},
}

@article {pmid41672620,
year = {2026},
author = {Wang, Y and Mi, Y and Zhang, X and Zhao, B and Li, H and Liu, X},
title = {[Review of Progress of Optical Brain-Computer Interface Technology and Prospects for Medical Device Testing Technology].},
journal = {Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation},
volume = {50},
number = {1},
pages = {54-63},
doi = {10.12455/j.issn.1671-7104.250262},
pmid = {41672620},
issn = {1671-7104},
mesh = {*Brain-Computer Interfaces ; Spectroscopy, Near-Infrared ; Humans ; Optogenetics ; },
abstract = {Optical brain-computer interface (OBCI) technology is a new cross-cutting frontier technology that achieves information interaction between the human brain and external devices through neuroengineering based on optical methods. Among them, brain-computer interface (BCI) devices based on functional near-infrared spectroscopy (fNIRS) have been successively developed and applied, and this type of technology has become one of the main development directions of non-invasive optical brain-computer interface medical device technology. At the same time, OBCI technology based on optogenetics and optical calcium imaging technology has also been proposed as a research hotspot in recent years, and will serve as a new direction for the future development of OBCI medical device testing technology. This paper mainly reviews the basic principles and research progress of functional near-infrared spectroscopy brain-computer interface (fNIRS-BCI), and introduces the research frontiers of BCI based on optogenetics and optical calcium imaging technology. Finally, it presents prospects for the further development of OBCI medical device testing technology, providing certain guidance and ideas for the development, testing, and supervision of the BCI industry.},
}

*Brain-Computer Interfaces
Spectroscopy, Near-Infrared
Humans
Optogenetics

@article {pmid41672352,
year = {2026},
author = {Wang, S and Xu, M and Qian, L and Gao, L and Sun, Y},
title = {Divergent effects of high-frequency rTMS on cognitive performance in sleep-deprived nurses: An EEG brain network study.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111772},
doi = {10.1016/j.brainresbull.2026.111772},
pmid = {41672352},
issn = {1873-2747},
abstract = {BACKGROUND: Sleep deprivation (SD) is a common occupational hazard, particularly for shift workers like nurses, leading to significant impairments in cognitive functions such as sustained attention and working memory. High-frequency repetitive transcranial magnetic stimulation (rTMS) is a promising neuromodulation technique for cognitive enhancement, but its effects in sleep-deprived individuals and the underlying neural mechanisms remain poorly understood. This study aimed to investigate the efficacy of high-frequency rTMS over the left dorsolateral prefrontal cortex (DLPFC) in modulating sustained attention and working memory after a night shift and to explore the associated changes in brain network topology.

METHODS: In a within-subject design, 28 healthy female night-shift nurses participated in two experimental sessions after a night of work: one with real 5 Hz rTMS and one with sham rTMS applied to the left DLPFC. Following stimulation, participants performed a psychomotor vigilance task (PVT) and a 2-back task while their electroencephalography (EEG) data were recorded. Behavioral performance (reaction time and accuracy) and subjective fatigue were assessed. Graph theory analysis was applied to the EEG data to evaluate changes in functional brain network topology at both global and nodal levels.

RESULTS: Real rTMS significantly reduced subjective mental fatigue compared to sham stimulation. However, the behavioral effects were task-dependent. For the 2-back task, real rTMS led to a significant impairment in performance, characterized by slower reaction times and lower accuracy. For the PVT, there was a non-significant trend towards improved performance. These behavioral outcomes were mirrored by distinct patterns of network reorganization. During the PVT, real rTMS induced decreased functional segregation (lower clustering coefficient and local efficiency) in the alpha band. Conversely, during the 2-back task, it resulted in increased functional segregation and small-worldness in the theta band.

CONCLUSION: High-frequency rTMS over the left DLPFC exerts differential, task-specific effects on cognitive function in a sleep-deprived state. The impairment in working memory, despite a network configuration theoretically supportive of local processing, likely results from an inverted-U effect, where the rTMS pushed an already strained and compensating brain system past its optimal level of cortical excitability. The findings highlight the critical role of both baseline brain state and specific cognitive demands in determining the outcomes of neuromodulation, providing crucial insights for the targeted application of rTMS to mitigate cognitive deficits from sleep deprivation.},
}

@article {pmid41671134,
year = {2026},
author = {Wang, C and Yang, L and Yuan, B and Zhang, J and Jin, C and Li, R and Bu, J},
title = {Subject-Adaptive EEG Decoding via Filter-Bank Neural Architecture Search for BCI Applications.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3663725},
pmid = {41671134},
issn = {2168-2208},
abstract = {Individual differences pose a significant challenge in brain-computer interface (BCI) research. Designing a universally applicable network architecture is impractical due to the variability in human brain structure and function. We propose Filter-Bank Neural Architecture Search (FBNAS), an EEG decoding framework that automates network architecture design for individuals. FBNAS uses three temporal cells to process different frequency EEG signals, with dilated convolution kernels in their search spaces. A multi-path NAS algorithm determines optimal architectures for multi-scale feature extraction. We benchmarked FBNAS on three EEG datasets across two BCI paradigms, comparing it to six state-of-the-art deep learning algorithms. FBNAS achieved cross-session decoding accuracies of 79.78%, 70.66%, and 68.38% on the BCIC-IV-2a, OpenBMI, and SEED datasets, respectively, outperforming other methods. Our results show that FBNAS customizes decoding models to address individual differences, enhancing decoding performance and shifting model design from expert-driven to machine-aided. The source code can be found at https://github.com/wang1239435478/FBNAS-master.},
}

@article {pmid41670873,
year = {2026},
author = {Silva, L and Lima, J and Delisle-Rodriguez, D and Bastos-Filho, T},
title = {A lower-limb motor imagery BCI using virtual reality and novel calibration strategy in post-stroke patients.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {41670873},
issn = {1741-0444},
}

@article {pmid41669281,
year = {2026},
author = {Todoroki, S and Phunruangsakao, C and Goto, K and Kutsuzawa, K and Owaki, D and Hayashibe, M},
title = {Deep Learning-Based Decoding and Feature Visualization of Motor Imagery Speeds From EEG Signals.},
journal = {IEEE open journal of engineering in medicine and biology},
volume = {7},
number = {},
pages = {27-34},
pmid = {41669281},
issn = {2644-1276},
abstract = {Objective: This study investigates the neurodynamics of motor imagery speed decoding using deep learning. Methods: The EEGConformer model was employed to analyze EEG signals and decode different speeds of imagined movements. Explainable artificial intelligence techniques were used to identify the temporal and spatial patterns within the EEG data related to imagined speeds, focusing on the role of specific frequency bands and cortical regions. Results: The model successfully decoded and extracted EEG patterns associated with different motor imagery speeds; however, the classification accuracy was limited and high only for a few participants. The analysis highlighted the importance of alpha and beta oscillations and identified key cortical areas, including the frontal, motor, and occipital cortices, in speed decoding. Additionally, repeated motor imagery elicited steady-state movement-related potentials at the fundamental frequency, with the strongest responses observed at the second harmonic. Conclusions: Motor imagery speed is decodable, though classification performance remains limited. The results highlight the involvement of specific frequency bands and cortical regions, as well as steady-state responses, in encoding MI speed.},
}

@article {pmid41668698,
year = {2026},
author = {Xu, T and Luo, F and Cui, Y and Zhou, Y},
title = {Editorial: Integrating multimodal approaches to unravel neural mechanisms of learning and cognition.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1753883},
pmid = {41668698},
issn = {1664-2295},
}

@article {pmid41668512,
year = {2026},
author = {Sun, S and Liu, D and Zhou, S and Wang, Y and Wang, H and Zheng, Z and Zhang, XD},
title = {Atomically Precise Clusterzymes: A Programmable Optoelectronic Platform for Neuroscience.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e19438},
doi = {10.1002/advs.202519438},
pmid = {41668512},
issn = {2198-3844},
support = {2021FJ-0009//Outstanding Youth Funds of Tianjin/ ; U24A6012//National Natural Science Foundation of China/ ; 82302381//National Natural Science Foundation of China/ ; 82302361//National Natural Science Foundation of China/ ; 82372028//National Natural Science Foundation of China/ ; 82102124//National Natural Science Foundation of China/ ; 82525035//National Science Fund for Distinguished Young Scholars/ ; YDZJSX20231A054//Central Guiding Local Science and Technology Development Fund Projects/ ; },
abstract = {Atomically precise metal clusters, characterized by their well-defined structures, have emerged as a versatile platform for energy, catalysis, and biomedicine. Building upon this foundation, the biocatalytic clusterzymes, a class of artificial enzymes with atomic-level programmable activity and renal-excreted properties, have successfully overcome the stability limitations of natural enzymes and biosafety concerns of conventional nanomaterials. This review systematically examines the synthesis, engineering principles, and applications of this programmable platform. First an in-depth analysis of the strategies is provided for programming biocatalytic or enzyme-like activity of metal clusters via atomic and ligand engineering. Meanwhile, infrared emissive metal clusters with tunable electronic structure and optical properties at the atomic level allow to achieve the pathological progression and clinical 3D visualization in deep tissue. Furthermore, semiconductor gold clusters with rich electron carriers can enhance the interface charge transfer between the metal electrode and surface molecular clusters, achieving highly sensitive neuron recording for an efficient brain computer interface. The clusters demonstrate great potential in neuroscience, including neuroinflammation, bioimaging, and neuromodulation. Finally, future challenges are outlined for the rational design and translational development of this programmable platform, poised to address complex challenges in biomedicine.},
}

@article {pmid41667798,
year = {2026},
author = {Zhao, C and Liao, Z and Jiang, D and Zhao, X and Yuan, B and Lin, R and Tang, J and Gong, B and Liao, J and Lin, L and Hu, Z},
title = {Parameter-efficient convolutional neural network for drug treatment outcome studies of pediatric epilepsy.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-39728-5},
pmid = {41667798},
issn = {2045-2322},
support = {SZGMTD2025001//Sanming Project of Medicine in Shenzhen Guangming/ ; 62271474//National Natural Science Foundation of China/ ; 321GJHZ2023246GC//International Partnership Program of the Chinese Academy of Sciences/ ; 2023B1515120007 and 2024A1515012138//Natural Science Foundation of Guangdong Province/ ; 2024B1212010010//Guangdong Provincial Key Laboratory of Multimodality Non-Invasive Brain-Computer Interfaces/ ; KJZD20230923113259001 and JCYJ20220530160005012//Shenzhen Science and Technology Program/ ; },
}

@article {pmid41667722,
year = {2026},
author = {De Schrijver, S and Garcia Ramirez, J and Iregui, S and Aertbeliën, E and De Schutter, J and Theys, T and Decramer, T and Janssen, P},
title = {An intracortical brain-machine interface based on macaque ventral premotor activity.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-38536-1},
pmid = {41667722},
issn = {2045-2322},
support = {C14/22/134//Onderzoeksraad, KU Leuven/ ; C14/22/134//Onderzoeksraad, KU Leuven/ ; C14/22/134//Onderzoeksraad, KU Leuven/ ; G.097422N//Fonds voor wetenschappelijk onderzoek Vlaanderen/ ; },
}

@article {pmid41666799,
year = {2026},
author = {Zhou, W and Zeng, T and Liu, D and Pang, R and Gong, L},
title = {Association of FIB-4 with orthostatic hypotension in Parkinson's disease.},
journal = {Autonomic neuroscience : basic & clinical},
volume = {264},
number = {},
pages = {103390},
doi = {10.1016/j.autneu.2026.103390},
pmid = {41666799},
issn = {1872-7484},
abstract = {BACKGROUND: Orthostatic hypotension (OH) is a common complication in Parkinson's disease (PD) patients, significantly impacting their quality of life. Recent evidence suggests a potential link between liver fibrosis, indicated by the Fibrosis-4 (FIB-4) index, and autonomic dysfunction. However, its relationship with OH in PD remains unexplored.

METHODS: A cross-sectional analysis was conducted using data from 1268 PD patients. The FIB-4 index was calculated based on age, AST, ALT, and platelet count. The association between FIB-4 and OH was assessed using multivariate logistic regression, with further curve fitting and subgroup analyses to test robustness.

RESULTS: The FIB-4 index was significantly associated with OH. For each 0.2-unit increase in FIB-4, the odds ratio (OR) for OH was 1.11 (95% CI: 1.05-1.17, p < 0.001). Tertile analysis showed ORs of 2.05 (95% CI: 1.27-3.31, p = 0.003) for T2 and 2.61 (95% CI: 1.64-4.17, p < 0.001) for T3, compared to T1. Curve fitting indicated a linear relationship, with no evidence of non-linearity. Sensitivity and subgroup analyses confirmed robustness.

CONCLUSIONS: Higher FIB-4 index values are independently associated with an increased risk of OH in PD patients, suggesting that liver fibrosis may contribute to OH development. Further longitudinal studies are needed to explore the underlying mechanisms.},
}

@article {pmid41666485,
year = {2026},
author = {Wang, H and Zhang, J and Ye, P and Yang, K and Xiong, J and Liu, X and Chen, T and Song, L},
title = {A knowledge-driven self-supervised learning method for enhancing EEG-based emotion recognition.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {199},
number = {},
pages = {108676},
doi = {10.1016/j.neunet.2026.108676},
pmid = {41666485},
issn = {1879-2782},
abstract = {Emotion recognition brain-computer interface (BCI) using electroencephalography (EEG) is crucial for human-computer interaction, medicine, and neuroscience. However, the scarcity of labeled EEG data limits progress in this field. To address this, self-supervised learning has gained attention as a promising approach. Despite its potential, self-supervised methods face two key challenges: (1) ensuring emotion-related information is effectively preserved, as its loss can degrade emotion recognition performance, and (2) overcoming inter-subject variability in EEG signals, which hinders generalization across subjects. To tackle these issues, we propose a novel knowledge-driven self-supervised learning framework for EEG emotion recognition. Our method incorporates domain knowledge to approximate the extraction of statistical feature differential entropy (DE), aiming to preserve emotion-related and generalizable information. The framework consists of two cascaded components as hard and soft alignments: a multi-branch convolutional differential entropy learning (MCDEL) module that simulates the DE extraction process, and a contrastive entropy alignment (CEA) module that exposes complex emotional semantics in high-dimensional space. Experiment results show that our method exhibits superior performance over existing self-supervised methods. The subject-independent mean accuracy and standard deviation of our method reached 84.48% ± 5.79 on SEED and 67.64% ± 6.35 and 68.63% ± 7.77 on the Arousal and Valence dimensions of DREAMER, respectively. We conduct an ablation study to demonstrate the contribution of each proposed component. Moreover, the t-SNE visualization intuitively presents the effect of our method on reducing inter-subject variability and discriminating emotional states.},
}

@article {pmid41666062,
year = {2026},
author = {Jin, J and Wang, H and Daly, I and Zhao, X and Li, S and Cichocki, A},
title = {A Fully Unsupervised Online Classification Algorithm for Event-Related Potential based Brain-Computer Interfaces.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3663323},
pmid = {41666062},
issn = {1558-2531},
abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) based on event-related potentials (ERPs) are among the most accurate and reliable BCIs. However, current mainstream classification algorithms struggle to eliminate the need for calibration and rely on expensive labeled data, limiting the practical usability of ERP based BCIs. The development of fully unsupervised algorithms is essential for the advancement of practical applications of BCI systems.

METHODS: In this study, we propose a novel unsupervised classification method called sliding-window distribution distance maximization (sDDM). This algorithm utilizes sliding windows to highlight important temporal features and transforms the metric of inter-class differences from absolute distances to relative distribution distances in Mahalanobis space, while incorporating information on target event similarity from the BCI paradigm. Additionally, our proposed spatial dimensionality reduction strategy ensures smaller spatial dimensions and more prominent spatial features.

RESULTS: We compare our proposed method to other state of-the-art unsupervised classification methods and evaluate it offline on our self-collected dataset, a public dataset recorded during the use of a P300 Speller by patients with ALS, and the BCI Competition III Dataset II. Our results demonstrate that our proposed method achieves the best spelling accuracy across all datasets, surpassing other unsupervised algorithms. We further explore its improvement effectiveness through ablation experiments.

CONCLUSION: Our proposed method enhances the performance of unsupervised classification in ERP-based BCIs.},
}

@article {pmid41666013,
year = {2026},
author = {Colque, CA and Lolle, S and La Rosa, R and Bendixen, MP and Rudjord-Levann, AM and Simões, FB and Aanæs, K and Molin, S and Johansen, HK},
title = {Cell-type-independent infection dynamics of clinical Pseudomonas aeruginosa isolates in human airway epithelial models.},
journal = {Cell reports},
volume = {45},
number = {2},
pages = {116951},
doi = {10.1016/j.celrep.2026.116951},
pmid = {41666013},
issn = {2211-1247},
abstract = {Persistent bacterial infections pose major clinical challenges, particularly in people with cystic fibrosis (pwCF), in whom Pseudomonas aeruginosa can persist for decades despite antibiotic treatment. To investigate the host-pathogen interactions influencing infection outcomes, we modeled P. aeruginosa infection using human airway epithelial cells cultured at the air-liquid interface from both CF and non-CF donors and the BCi-NS1.1 cell line. Infection assays with reference and clinical strains revealed four distinct infection clusters based on virulence, epithelial damage, and localization, independent of model type or strain lineage. Correction of CF transmembrane conductance regulator protein (CFTR) channel function did not alter infection outcomes. Dual RNA sequencing showed conserved host inflammatory responses across models, while bacterial transcriptional profiles varied by host context, particularly in CF models. These findings demonstrate that while infection outcomes and host transcriptional responses are consistent across models, bacterial adaptation in the CF airways drives transcriptional reprogramming linked to persistence in pwCF.},
}

@article {pmid41664905,
year = {2026},
author = {Lin, D and Sun, H and Shen, Q and Jiang, X and Fu, S and Xiao, Q and Wu, S and Klucharev, V and Shestakova, A and Wang, Y},
title = {Decision for self and other modulates risk attitude and electrophysiological processing: evidence from a behavioral and electrophysiological experiment.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {36},
number = {2},
pages = {},
doi = {10.1093/cercor/bhag001},
pmid = {41664905},
issn = {1460-2199},
support = {72371165//Natural Science Foundation of China/ ; 71971199//Natural Science Foundation of China/ ; ZD20240026//Key Project of Hangzhou Health Science and Technology Plan/ ; 2023KFKT006//Open Research Fund of Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
mesh = {Humans ; Male ; Female ; *Risk-Taking ; *Decision Making/physiology ; Electroencephalography ; Young Adult ; Adult ; *Event-Related Potentials, P300/physiology ; *Brain/physiology ; Gambling/psychology ; *Attitude ; Adolescent ; Evoked Potentials/physiology ; },
abstract = {Understanding how people make risky decisions for others compared to themselves is central to decision neuroscience. However, the cognitive and neural underpinnings of such self-other shifts in risk preference-and the mechanisms driving individual differences-remain unclear. To address this, we employed a mixed gambling task with feedback in which participants made risky decisions for themselves and for others while electroencephalography was recorded. Although individuals generally exhibited similar patterns across agents, decisions made for others were associated with a higher degree of risk-taking compared to those made for oneself. In terms of individual heterogeneity, predispositions and decision weights derived from the drift-diffusion model accounted for individual differences and agent-specific shifts in risk preferences. The event-related potential (ERP) component P300 was significantly modulated by the agent, valence, and risk attitude. Critically, risk-averse individuals showed larger P300 deflections and greater amplitude differences between the self and other conditions, whereas risk-seeking individuals exhibited smaller and more uniform P300 responses across agents. Together, these findings highlight both shared and distinct behavioral and neural mechanisms underlying risky decision-making for self and others and underscore the potential of ERP components as neural markers of decision-making under risk in social contexts.},
}

Humans
Male
Female
*Risk-Taking
*Decision Making/physiology
Electroencephalography
Young Adult
Adult
*Event-Related Potentials, P300/physiology
*Brain/physiology
Gambling/psychology
*Attitude
Adolescent
Evoked Potentials/physiology

@article {pmid41664110,
year = {2026},
author = {Zhang, J and Han, S and Shen, Y and Wu, X and Zhao, Y and Wu, Z and Luo, N and Yang, Z and Li, D and Song, M and Wu, P and Tao, DD and Liu, J and Li, Y and Jiang, T},
title = {Digital twin brain reveals state-specific stimulation targets for abnormal brain dynamics in tinnitus.},
journal = {BMC medicine},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12916-026-04687-1},
pmid = {41664110},
issn = {1741-7015},
support = {K2024079//Jiangsu Commission of Health/ ; SSD2024025//Suzhou Basic Research Pilot Project/ ; 82171159//National Natural Science Foundation of China/ ; K2023027//Key Program of Jiangsu Commission of Health/ ; ML12203423//Medicine Plus X Project from Suzhou Medical School of Soochow University/ ; 2021ZD0200201//STI2030-Major Projects/ ; 82151307//National Science Foundation of China/ ; 2022ND0AN01//Scientific Project of Zhejiang Lab/ ; },
abstract = {BACKGROUND: Tinnitus affects 10-15% of adults globally, yet there are still no effective treatments for this major health condition. Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuromodulation technique, allows modulation of pathologically altered functional activities to promote symptom remission. However, its efficacy critically depends on the selection of stimulation targets, and substantial interindividual variability has been observed in clinical trials. Here, we aimed to identify potential target regions that are causally involved in alleviating distinct functional abnormalities using the digital twin brain (DTB).

METHODS: A cohort of 89 participants was used to characterize whole-brain neural activity patterns. Multimodal neuroimaging data were used to develop the tinnitus-specific DTB and to generate causal response maps based on more than 1.64 million virtual stimulations. Whole-brain gene expression data were further integrated to examine the neurobiological plausibility of the DTB-derived causal response maps. Finally, we validated the predictive capacity of such response maps using an independent rTMS dataset.

RESULTS: We identified two aberrant brain states that emerged sequentially with disease progression, predominantly overlapping with the somatomotor and default mode networks, respectively. DTB-derived causal response maps revealed that the modulation of sensory and cognitive states requires stimulation of distinct, functionally specialized regions. Specifically, parieto-occipital regions play a crucial role in sensory modulation, while the dorsolateral prefrontal cortex exerts a causal influence on cognitive modulation. Moreover, these causal response maps correlate with the expression of tinnitus risk genes. By incorporating individual connectivity profiles of target regions, DTB-derived causal response maps accurately predicted rTMS effects on both sensory state (r > 0.85, Ppermutation < 0.01) and cognitive state (r > 0.78, Ppermutation < 0.05). Particularly, the predictive capacity exhibited a state-specific nature.

CONCLUSIONS: This work suggests that brain functional alterations in tinnitus evolve with disease progression, and DTB has the potential to predict rTMS effects on distinct brain states, thereby informing more precise and targeted noninvasive brain stimulation interventions for tinnitus.

TRIAL REGISTRATION: Trial registered with https://www.chictr.org.cn/indexEN.html, Explore the mechanism of repetitive transcranial magnetic stimulation intervention in tinnitus based on multi-modal functional magnetic resonance imaging (ChiCTR2100047989), Submitted June 2021, First Patient Enrolled July 2021.},
}

@article {pmid41663680,
year = {2026},
author = {Valente, M and Branco, D and Bermúdez I Badia, S and Fernandes, JC and Figueiredo, P and Vourvopoulos, A},
title = {EEG-based predictors of motor recovery during immersive VR-BCI rehabilitation.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-026-39106-1},
pmid = {41663680},
issn = {2045-2322},
support = {10.54499/2022.02283.PTDC//Fundação para a Ciência e a Tecnologia/ ; },
abstract = {Motor impairment following stroke frequently leads to long-term disability, limiting independence and quality of life. Brain-Computer Interface (BCI) systems integrating motor imagery (MI) with virtual reality (VR) offer promising avenues for enhancing neuroplasticity and engagement through immersive, real-time, and proprioceptive feedback. Yet, identifying reliable electroencephalography (EEG)-based biomarkers that reflect or predict recovery remains challenging. This study investigated the relationship between event-related desynchronization (ERD) dynamics during MI-VR training and motor recovery in individuals with chronic stroke. Fourteen participants with stroke (9 experimental, 5 control) completed a 4-week VR-BCI intervention and were compared with a non-stroke reference cohort (N = 35). Linear mixed-effects models assessed ERD modulation across sessions and groups, and a two-stage regression evaluated the predictive value of ERD features for Fugl-Meyer Assessment (FMA) gains. Results showed no significant ERD change across sessions, but stroke participants exhibited significantly reduced ERD compared to controls. Baseline ERD amplitude predicted motor improvement, whereas ERD progression did not. Ipsilateral ERD showed a compensatory trend in ischemic stroke. These findings indicate that baseline ERD may serve as a stronger prognostic biomarker than short-term ERD dynamics, supporting the development of personalized VR-BCI rehabilitation strategies for chronic stroke recovery.},
}

@article {pmid41662697,
year = {2026},
author = {Cummins, DD and Barth, K and Ho, E and Fink Skular, A and Dister, J and Rapoport, B and Saez, I and Bederson, JB},
title = {High-resolution cortical mapping within and across the central sulcus using 1024-electrode micro-electrocorticography arrays: illustrative case.},
journal = {Journal of neurosurgery. Case lessons},
volume = {11},
number = {6},
pages = {},
pmid = {41662697},
issn = {2694-1902},
abstract = {BACKGROUND: Central sulcus identification using phase reversal on electrocorticography (ECoG) is a critical tool for neurosurgical intervention around the primary motor and somatosensory cortices. This mapping is typically performed using cortical arrays with a resolution of several millimeters.

OBSERVATIONS: A 30-year-old female underwent a right frontoparietal craniotomy for resection of a 4-cm contrast-enhancing lesion within the central sulcus. Central sulcus localization was performed using a standard ECoG array. High-resolution micro-ECoG (µECoG) arrays were then placed over the pre- and postcentral gyri, giving 2048-electrode recordings across the central sulcus. Combining this high-resolution µECoG with an augmented reality imaging overlay to identify the tumor, the central sulcus was split, revealing the underlying tumor. A safe, gross-total resection was obtained with no postoperative complications. Through the use of µECoG arrays spanning into the central sulcus, a high-resolution phase-reversal contour was identified across the central sulcus.

LESSONS: The authors demonstrate the feasibility and utility of µECoG for sensorimotor mapping within the central sulcus, revealing a phase reversal at a resolution of approximately 400 microns. Compared to standard mapping, which records gyral surface electrophysiology, they further demonstrate phase-reversal electrophysiology within a dissected central sulcus. High-resolution cortical mapping from µECoG may foster several neurosurgical advancements, from tumor resection to brain-computer interfaces. https://thejns.org/doi/10.3171/CASE25534.},
}

@article {pmid41657963,
year = {2026},
author = {Cao, Y and Yang, H and Xue, Y and Wang, F and Li, T and Zhao, L and Fu, Y},
title = {Ethical risks and considerations of the integration of Brain-Computer Interfaces with Artificial Intelligence.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {46},
pmid = {41657963},
issn = {1871-4080},
abstract = {In recent years, with the rapid development of Brain-Computer Interface (BCI) and Artificial Intelligence (AI) technologies, a trend of increasing integration between the two has emerged. In some practical applications, BCI has been combined with AI to enhance the overall performance of systems, including usability, user experience, and satisfaction, especially in terms of intelligent capabilities. However, this technological integration also introduces new or exacerbates existing ethical risks, such as neural privacy breaches, cross-domain misuse, and unclear system responsibility attribution. This paper discusses the novel or more severe ethical challenges arising from the fusion of BCI and AI technologies, as well as measures and strategies to address these ethical issues, calling for the establishment of more comprehensive ethical guidelines and governance frameworks. It is hoped that this paper will contribute to a deeper understanding and reflection on the ethical risks and corresponding regulations related to the integration of BCI and AI technologies.},
}

@article {pmid41657959,
year = {2025},
author = {Bennett, P and Barr, N},
title = {Neurorehabilitation technologies and functional recovery after brain injury: influence of sex, an integrative review.},
journal = {Frontiers in digital health},
volume = {7},
number = {},
pages = {1677873},
pmid = {41657959},
issn = {2673-253X},
abstract = {BACKGROUND: Acquired brain injury (ABI), which includes traumatic brain injury (TBI) and stroke, is a leading cause of disability. Evidence shows that sex may influence functional recovery post-acquired brain injury, potentially due to biological (e.g., hormones) and social factors (e.g., caregiver availability). Meanwhile, new neurorehabilitation technologies-such as virtual reality, robotic-assistance, and brain-computer interfaces-offer promising avenues for improving functional outcomes. Understanding how these technologies interact with sex differences could advance equitable and personalized healthcare.

RESEARCH QUESTION: Does evidence support a rationale for studying, developing, or employing neurorehabilitation technologies differently in males and females to improve functional outcomes post-ABI?

METHODOLOGY: An empirical integrative narrative review was conducted. Searches were performed in PubMed, Cochrane Library, and OVID, focusing on adult populations with ABI. Key terms encompassed "acquired brain injury," "sex differences," and "neurorehabilitation technologies." Fifty-nine studies met inclusion criteria, spanning diverse methodologies, settings, and cultural contexts. Data were synthesized to compare functional outcomes impacted by sex and by neurorehabilitation technologies.

RESULTS: Findings indicate that the effect of sex on neurorehabilitation outcomes is multifaceted. Studies using functional independence measures often reported no significant sex differences, whereas more specific measures (e.g., those measuring cognitive or social functions) identified notable sex effects. Neurorehabilitation technologies showed positive outcomes in various functional domains (e.g., upper extremity motor function, gait, cognition), but most studies focused on stroke.

DISCUSSION: Current research does not support the use of sex-differentiated technology interventions to target upper extremity motor function or global functional independence post-stroke. Sex-differentiated treatment may be relevant for other functional domains such as cognitive recovery, psychological well-being and social outcomes, but this requires further research, particularly for non-stroke ABI.

CONCLUSION: These findings suggest that some neurorehabilitation technologies can be applied without sex-specific modification, whereas others may benefit from sex-specific considerations. Owing to methodological limitations and sparse data, especially for TBI, additional investigations are warranted. As novel neurorehabilitation technologies evolve, accounting for sex differences may enhance personalized care and optimize long-term outcomes.},
}

@article {pmid41657359,
year = {2025},
author = {Lyu, R},
title = {Deep learning approaches for EEG-based healthcare applications: a comprehensive review.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1689073},
pmid = {41657359},
issn = {1662-5161},
abstract = {Electroencephalography (EEG) is a longstanding means of non-invasively recording brain signals and has become highly valuable for the study of neurological and cognitive processes. Recent progress in deep learning has also greatly improved both EEG signal analysis and interpretation, making more accurate, reliable and scalable solutions in various healthcare applications. In this review, we present a comprehensive summary of the convergence of EEG and deep learning, with an emphasis on diagnostic of neurological disorders, brain recovery, mental health conditions, and brain-computer interface (BCI) applications. We methodically investigate the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, transformer models and hybrid architectures for EEG-based tasks. Key challenges that have been hampering emerging solutions are critically covered, namely signal-related variability, the lack of data, and deep learning model limited interpretability. Finally, we highlight emerging trends, open issues and promising research directions, with the aim of laying a solid ground toward the improvement of EEG-based healthcare applications and to drive future research in this fast-growing research area.},
}

@article {pmid41656193,
year = {2026},
author = {Yan, S and Li, Q and Li, R and Zhang, L and Zhang, R and Chen, M and Li, M and Li, R and Zhang, H and Shi, L and Hu, Y},
title = {Prognosis prediction of patients with disorders of consciousness based on digital twin brain models.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01905-y},
pmid = {41656193},
issn = {1743-0003},
support = {2022ZD0208500//the STI 2030-Major Project/ ; 62173310//the National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: The accurate prediction of prognosis in patients with disorders of consciousness (DOC) is a significant challenge in clinical practice. Some studies based on traditional electroencephalography (EEG) features have shown potential for DOC prognosis. However, the underlying mechanisms behind the recovery of patients with DOC still lack in-depth research.

METHODS: In this study, we used mathematical tools to construct digital twin brain models (DTBM) for DOC patients with different outcomes. Then, we trained a support vector machine classifier using model parameters and modal controllability features to distinguish between DOC patients with different outcomes, and assessed the importance of these features. Finally, we used a support vector machine regressor to predict the Coma Recovery Scale-Revised (CRS-R) score at 6-month follow-up.

RESULTS: The results showed that the prognosis model based on local model parameters and modal controllability features achieved better performance (AUC = 90.22%, F-score = 86.00%, SEN = 84.31%, SPE = 91.43%) than the prognosis models based on some traditional EEG features. Additionally, a positive prognosis is associated with lower levels of inhibitory gain, higher levels of excitatory gain and modal controllability, particularly in brain regions within the frontoparietal network. In 74% and 70% of UWS and MCS patients, the MAE between the predicted CRS-R score and the actual CRS-R score was less than 5.

CONCLUSIONS: Overall, our study contributes to enriching the neuromarkers associated with DOC prognosis and further elucidates the neural mechanisms of consciousness recovery.},
}

@article {pmid41655642,
year = {2026},
author = {Wang, J and Li, M},
title = {TPCNet: A Temporal Periodicity Convolutional Network for motor imagery EEG decoding in stroke patients.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110707},
doi = {10.1016/j.jneumeth.2026.110707},
pmid = {41655642},
issn = {1872-678X},
abstract = {BACKGROUND: Stroke caused by vascular rupture or blockage has high incidence and leads to significant disability. Motor imagery (MI) electroencephalogram (EEG) is a promising approach to understanding and addressing stroke-related motor impairments. However, the practical application of EEG-based rehabilitation is hindered by an insufficient understanding of the task-specific features and complex temporal patterns inherent in the EEG signals of stroke patients.

NEW METHOD: In this study, we collected EEG signals from 24 stroke patients performing four unilateral upper limb MI tasks. Among them, 12 subjects performed forward arm raising and lowering, while the remaining 12 performed lateral arm raising and lowering. Moreover, we propose a Temporal Periodicity Convolutional Network (TPCNet) for EEG-based MI classification. TPCNet consists of a convolutional block for extracting shallow spatiotemporal features, a sliding window structure that ensures consistent action initiation across samples, and a temporal periodicity block for capturing variations in periodic patterns associated with MI tasks.

RESULTS: TPCNet achieved a classification accuracy of 86.53% on the stroke patient MI dataset and 82.21% on the BCI Competition IV 2a dataset (left hand, right hand, feet, and tongue). Gradient-weighted Class Activation Mapping (Grad-CAM) analysis suggests that stroke patients may exhibit longer task-specific MI periodicity than healthy subjects.

The proposed method achieves superior performance on stroke patient MI tasks and competitive results on public MI datasets involving healthy subjects.

CONCLUSIONS: The proposed TPCNet model effectively captures the spatiotemporal features and periodic patterns of EEG signals, leading to enhanced classification accuracy.},
}

@article {pmid41654951,
year = {2026},
author = {Xu, C and Zhu, L and Lai, J and Luo, Z and Ying, J and Hu, S and Song, P and Yang, J},
title = {Global and regional quality of care index in major depressive disorder: the global burden of disease study 2021.},
journal = {International journal for equity in health},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12939-026-02775-5},
pmid = {41654951},
issn = {1475-9276},
support = {2023YFC2506200//National Key Research and Development Program of China/ ; 2024C03098, 2025C02109//Key Research and Development Program of Zhejiang Province/ ; 2025C01104//Key Research and Development Program of Zhejiang Province/ ; },
abstract = {BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability, yet systematic evaluations of quality of care disparities across regions are sparse. Leveraging data from the Global Burden of Disease (GBD) Study 2021, this study quantified the quality of care for MDD from 1990 to 2021 and examined socio-demographic inequities by age and sex.

METHODS: Data on MDD were extracted from the GBD 2021 study for the globe, 5 socio-demographic index (SDI) regions and 21 GBD regions. The quality of care index (QCI) is a composite, dimensionless index scaling from 0 to 100, with higher values indicating better quality of care. The age-standardized QCI was calculated using the Principal Component Analysis (PCA) method and further stratified by sex, age, and region. The gender disparity ratio (GDR) was used to characterize the sex disparities. The temporal trend of QCI and GDR by sex and age across SDI regions was further calculated.

RESULTS: Globally, the QCI of MDD increased from 56.26 (1990) to 62.95 (2021), with low SDI regions consistently exhibiting the highest QCI (71.90 in 1990; 71.19 in 2021) and high SDI regions the lowest (40.28 to 51.55). Sex disparities widened as female QCI rose by 14.0% (vs. 7.6% in males) and GDR increased from 1.02 to 1.08. The highest GDR (1.27) persisted in Oceania, while Tropical Latin America had the lowest (0.94 in 2021). Age-specific QCI peaked in adolescents (10-14 years) and declined with age, with notable improvements post-2019. Older adults (> 80 years) in high SDI regions saw higher QCI versus low-middle SDI regions. Trend analysis revealed that high and high-middle SDI regions maintained a lower QCI of MDD than the global average level but narrower sex gaps (GDR 1.04 in 2021) compared to low SDI regions (GDR 1.15).

CONCLUSIONS: While global quality of care for MDD improved, socioeconomic development inversely correlated with QCI, potentially reflecting systemic under-reporting in low-resource settings and overburdened systems in high-income regions. Persistent gender and age disparities necessitate targeted and equal policies, including sex-sensitive care models and geriatric mental health integration.},
}

@article {pmid41653675,
year = {2026},
author = {Li, Q and Liu, Y and Zhao, N and Yuan, Y and He, R},
title = {A novel ECG QRS complex detection algorithm based on dynamic Bayesian network.},
journal = {Artificial intelligence in medicine},
volume = {174},
number = {},
pages = {103370},
doi = {10.1016/j.artmed.2026.103370},
pmid = {41653675},
issn = {1873-2860},
abstract = {Accurate detection of the QRS complex, a crucial reference for heartbeat localization in electrocardiogram (ECG) signals, remains inadequate in wearable ECG devices due to complex noise interference. In this study, we propose a novel QRS complex detection method based on dynamic Bayesian network (DBN), integrating the probability distribution of RR intervals. Unlike methods focusing solely on ECG waveforms, our approach explicitly integrates ECG waveform and heart rhythm information into a unified probability model, enhancing noise robustness. Additionally, an unsupervised parameter optimization using expectation maximization (EM) adapts to individual differences of patients. Furthermore, several simplification strategies improve reasoning efficiency, and an online detection mode enables real-time applications. Our method outperforms other state-of-the-art QRS detection methods, including deep learning (DL) methods, on noisy datasets. In conclusion, the proposed DBN-based QRS detection algorithm demonstrates outstanding accuracy, noise robustness, generalization ability, real-time capability, and strong scalability, indicating its potential application in wearable ECG devices.},
}

@article {pmid41401084,
year = {2026},
author = {Ying, W and Yu, J and Wang, X and Liu, J and Deng, B and Shao, X and Wang, J and Tao, T and Cao, J and He, Q and Yang, B and Chen, Y and Ying, M},
title = {Therapeutic targeting of YOD1 disrupts the PAX-FOXO1/N-Myc feedback loop in rhabdomyosarcoma.},
journal = {JCI insight},
volume = {11},
number = {3},
pages = {},
doi = {10.1172/jci.insight.193221},
pmid = {41401084},
issn = {2379-3708},
mesh = {Humans ; Animals ; Mice ; *Rhabdomyosarcoma/genetics/pathology/metabolism/drug therapy ; Cell Line, Tumor ; Forkhead Box Protein O1/metabolism/genetics ; *N-Myc Proto-Oncogene Protein/metabolism/genetics ; Oncogene Proteins, Fusion/metabolism/genetics ; Gene Expression Regulation, Neoplastic ; Signal Transduction ; Paired Box Transcription Factors/metabolism ; Feedback, Physiological ; Xenograft Model Antitumor Assays ; Proto-Oncogene Proteins c-myc ; },
abstract = {Fusion-positive rhabdomyosarcoma (FP-RMS), driven by PAX-FOXO1 fusion oncoproteins, represents the subtype of RMS with the poorest prognosis. However, the oncogenic mechanisms and therapeutic strategies of PAX-FOXO1 remain incompletely understood. Here, we discovered that N-Myc, in addition to being a classic downstream target of PAX-FOXO1, can also activate its expression and form a transcriptional complex with PAX-FOXO1, thereby markedly amplifying oncogenic signaling. The reciprocal transcriptional activation of PAX3-FOXO1 and N-Myc is critical for FP-RMS malignancy. We further identified YOD1 as a deubiquitinating enzyme that stabilizes both PAX-FOXO1 and N-Myc. Knocking down YOD1 or inhibiting it with G5 could suppress FP-RMS growth both in vitro and in vivo, through promoting the degradation of both PAX-FOXO1 and N-Myc. Collectively, our results identify that YOD1 promotes RMS progression by regulating the PAX3-FOXO1/N-Myc positive feedback loop, and highlight YOD1 inhibition as a promising therapeutic strategy that concurrently reduces the levels of both oncogenic proteins.},
}

Humans
Animals
Mice
*Rhabdomyosarcoma/genetics/pathology/metabolism/drug therapy
Cell Line, Tumor
Forkhead Box Protein O1/metabolism/genetics
*N-Myc Proto-Oncogene Protein/metabolism/genetics
Oncogene Proteins, Fusion/metabolism/genetics
Gene Expression Regulation, Neoplastic
Signal Transduction
Paired Box Transcription Factors/metabolism
Feedback, Physiological
Xenograft Model Antitumor Assays
Proto-Oncogene Proteins c-myc

@article {pmid40031285,
year = {2026},
author = {Candia-Rivera, D and Faes, L and De Vico Fallani, F and Chavez, M},
title = {Measures and Models of Brain-Heart Interactions.},
journal = {IEEE reviews in biomedical engineering},
volume = {19},
number = {},
pages = {24-40},
doi = {10.1109/RBME.2025.3529363},
pmid = {40031285},
issn = {1941-1189},
mesh = {*Brain-Computer Interfaces ; Biomarkers/analysis ; *Signal Processing, Computer-Assisted ; *Brain/physiology ; *Heart/physiology ; Humans ; Animals ; *Models, Neurological ; },
abstract = {Exploring brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, has demonstrated enormous potential in biomarker development and neuroscientific research. A range of techniques, from molecular to behavioral approaches, has been proposed to measure these interactions. Different frameworks use signal processing techniques, from estimating brain responses to individual heartbeats to interactions linking the heart to changes in brain organization. This review provides an overview of the most notable signal processing strategies currently used for measuring and modeling brain-heart interactions. It discusses their usability and highlights the main challenges that need to be addressed for future methodological developments. Current methodologies have deepened our understanding of the impact of physiological disruptions on brain-heart interactions, solidifying it as a biomarker. The vast outlook of these methods could provide tools for disease stratification in neurological and psychiatric disorders. As we tackle new methodological challenges, gaining a more profound understanding of how these interactions operate, we anticipate further insights into the role of peripheral neurons and the environmental input from the rest of the body in shaping brain functioning.},
}

*Brain-Computer Interfaces
Biomarkers/analysis
*Signal Processing, Computer-Assisted
*Brain/physiology
*Heart/physiology
Humans
Animals
*Models, Neurological

@article {pmid41651814,
year = {2026},
author = {Zhang, Y and Zhao, M and Song, S and Chen, Q and Meng, Y and Yu, X and Wei, W and Deng, W and Guo, W and Li, T and Qi, X},
title = {Blood circulating cell-free mitochondrial DNA as a potential biomarker for major depressive disorder: a meta-analysis.},
journal = {Translational psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41398-026-03865-2},
pmid = {41651814},
issn = {2158-3188},
support = {82371524//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82301709//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81920108018, 82230046//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {BACKGROUND: Mitochondrial dysfunction has been implicated in major depressive disorder (MDD), but reliable, measurable biomarkers remain elusive. As a minimally invasive and quantifiable biomarker, circulating cell-free mitochondrial DNA (ccf-mtDNA) in blood offers potential for objective assessment of mitochondrial stress in MDD. However, evidence linking regarding the association between ccf-mtDNA levels and MDD is limited and inconsistent.

METHODS: We systematically searched eight databases, including PubMed, EMBASE, and major Chinese repositories. Thirteen studies with 1370 participants (837 individuals with MDD and 533 controls) were included per PRISMA guidelines. P-values were synthesized using the Lipták-Stouffer Z-score method. Sensitivity and fail-safe N analyses assessed the robustness of the findings and publication bias, and stratified analyses examined the effects of age, antidepressant use, and geographic region.

RESULTS: Across studies, elevated blood ccf-mtDNA levels were significantly associated with MDD (p = 0.013). Stratified analyses revealed stronger associations in older adults (≥60 years old; p = 0.0009), unmedicated patients (p = 4.99 × 10⁻⁶), and North American cohorts (p = 4.29 × 10⁻¹¹), but not in younger individuals (p = 0.83), medicated patients (p = 0.97), and Asian/European samples (p = 0.72, p = 0.99). Sensitivity analyses indicated moderate instability overall but confirmed data robustness in key subgroups.

CONCLUSIONS: This is the first meta-analysis to establish a significant link between elevated blood ccf-mtDNA and MDD, highlighting age and antidepressant exposure as critical modulators. These findings support the potential of blood ccf-mtDNA to serve as a biomarker for late-life and drug-naïve depression, with implications for objective diagnosis and personalized treatment.},
}

@article {pmid41649534,
year = {2026},
author = {Li, H and Liu, J and Li, J},
title = {Cross-subject motor imagery EEG signal classification based on meta-transfer learning.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-12},
doi = {10.1080/10255842.2026.2626477},
pmid = {41649534},
issn = {1476-8259},
abstract = {Motor imagery (MI) EEG classification, a core BCI task, faces challenges due to EEG's low signal-to-noise ratio and non-stationarity. Traditional supervised learning methods perform poorly in cross-subject and small-sample scenarios, limiting practical use. We propose CMHA-Net, a MI-EEG-optimized CNN integrating depthwise separable convolution, deep convolution and multi-head attention, combined with a Meta-SGD-based meta-transfer learning framework. Experiments on BCI-IV-2a and High Gamma datasets show 81.61% and 88.15% accuracy, outperforming existing models by 4-15% and excelling in small-sample cases, advancing clinical and real-world BCI applications.},
}

@article {pmid41649064,
year = {2026},
author = {Murphy, A and Schaly, S and Chiu, D and Mitchell, A and Shen, C and Maddirala, G and Singh, T and McEwan, A and Karlsson, P},
title = {Access technologies for people with significant motor impairment with potential to impact speed and/or accuracy of communication: a scoping review.},
journal = {Augmentative and alternative communication (Baltimore, Md. : 1985)},
volume = {},
number = {},
pages = {1-13},
doi = {10.1080/07434618.2026.2619160},
pmid = {41649064},
issn = {1477-3848},
abstract = {Individuals with significant communication and physical impairments often rely on augmentative and alternative communication (AAC) to facilitate independent communication across a range of communication partners and settings. Due to physical impairments, many individuals require alternate methods to access AAC systems, known as access technologies. While access technologies have advanced, they remain considerably slower than verbal communication. This scoping review explored recent advances in access technologies published between 2019 and 2024, focusing on technologies that facilitate communication speed and/or accuracy for individuals of any age with physical disabilities. Forty-six studies met inclusion criteria, covering a range of technologies such as brain-computer interfaces (BCIs), eye-tracking technology, and novel applications, such as a mixed reality AAC environment and multimodal access approaches (e.g., integrated eye-tracking with switch scanning, hybrid BCI eye-tracking). Despite technological progress, fewer than one-third of studies addressed the role of communication partners in setup and support, highlighting a gap in user-centred design. Findings are discussed in terms of practical applications and emerging directions for technology development. Implications for clinical practice and future research include the need for inclusive design, improved usability, and greater consideration of communication partner involvement in AAC access solutions.},
}

@article {pmid41648134,
year = {2026},
author = {Levin, AD and Avansino, DT and Kamdar, FB and Card, NS and Wairagkar, M and Jacques, BG and Jude, JJ and Iacobacci, C and Lacayo, BE and Bechefsky, PH and Nason-Tomaszewski, SR and Deo, DR and Hochberg, LR and Rubin, DB and Williams, ZM and Brandman, DM and Stavisky, SD and AuYong, N and Pandarinath, C and Linderman, SW and Henderson, JM and Willett, FR},
title = {Cross-brain transfer of high-performance intracortical speech and handwriting BCIs.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.01.12.699110},
pmid = {41648134},
issn = {2692-8205},
abstract = {Intracortical brain-computer interfaces (BCIs) that decode complex movements, such as handwriting and speech, can require substantial training data to achieve high performance. We investigated whether leveraging the neural activity recordings of previous users could reduce this initial data collection burden for new BCI users (an approach we call "cross-brain transfer"). Using intracortical recordings from five BrainGate2 clinical trial participants, we tested cross-brain transfer for both speech and handwriting neural decoders trained and evaluated on general, unconstrained corpora of spoken and written English. We found that cross-brain transfer improved decoding performance when training data from the target user was limited (< 200 sentences), and that dataset-specific input layers to the decoder were critical for combining data across users. Without trainable input layers, transfer failed and performed worse than training from scratch on target user data only. Finally, we measured the effectiveness of cross-brain transfer relative to training with (1) more data from the same user and (2) more electrode-permuted data from the same user, which simulates sampling from another brain with identical neural latent structure. In some cases (T16 speech, T12 handwriting), cross-brain transfer appeared as effective as additional permuted data from the same user, while in others (T12 speech, T15 speech) electrode-permuted data was more beneficial. Our results successfully demonstrate and characterize cross-brain transfer learning between multiple intracortical BCI users, for both speech and handwriting, using a general open-ended dataset not restricted to small sets of words or phrases. This work highlights a promising path towards addressing a key barrier to the clinical translation of BCIs, while clarifying when cross-brain transfer may be most beneficial and the decoder design choices needed to realize those gains.},
}

@article {pmid41647146,
year = {2026},
author = {Zhu, L and Jiang, P and Huang, A and Zhang, J and Yuan, P},
title = {M3T-attention: a multi-level multi-scale temporal attention transformer for EEG hand movement trajectory decoding.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {33},
pmid = {41647146},
issn = {1871-4080},
abstract = {In recent years, brain-computer interface (BCI) technology has made significant progress in the fields of neural engineering and human-computer interaction. Among these advances, decoding upper-limb movements from electroencephalography (EEG) signals has become a key research focus. However, most existing studies concentrate on discrete classification tasks (e.g., motor imagery recognition), while the prediction of three-dimensional continuous movement trajectories still faces several major challenges. These include the low signal-to-noise ratio of EEG signals, substantial inter-subject variability that limits generalizability, and the high degrees of freedom in 3D trajectories, which increase decoding complexity. To address these challenges and improve the accuracy of decoding continuous 3D hand movement trajectories from EEG signals, this study proposes a Multi-level Multi-scale Temporal Attention Transformer framework (M3T-Attention). The model is designed to extract temporal features across multiple time scales from EEG signals and integrate them via cross-scale attention mechanisms, enabling a nonlinear mapping from 0.5 to 12 Hz EEG signals to 3D kinematic parameters (position, velocity, and acceleration). The model was trained using EEG and wrist kinematic data from the WAY-EEG-GAL dataset. Experimental results show that the proposed method achieves Pearson correlation coefficients (PCCs) of 0.8816, 0.8841, and 0.8711 on the X, Y, and Z axes, respectively, demonstrating robust prediction performance across all subjects and outperforming existing state-of-the-art approaches. In summary, through comparative experiments, statistical significance analysis, and ablation studies, we have fully verified its ability to capture neural coding patterns. It significantly enhances the decoding performance from EEG signals to movement trajectories, offering new approaches for BCI applications in complex motor control scenarios. We have made the model's source code publicly available on GitHub repository URL: https://github.com/jjspp/M3T_Attention.},
}

@article {pmid41647142,
year = {2026},
author = {Pan, H and Teng, B and Liu, Z and Tong, S and Yu, X and Li, Z},
title = {Five-class motor imagery BCI classification and its application to brain-controlled wheelchairs.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {38},
pmid = {41647142},
issn = {1871-4080},
abstract = {Brain-controlled wheelchair (BCW) technology enables direct wheelchair control via brain-computer interfaces (BCIs), eliminating the need for physical limb interaction. Motor imagery-based BCIs (MI-BCIs) are widely used in non-invasive BCIs due to their ability to provide intuitive neural control without external stimuli. However, developing a BCW system based on MI-BCIs remains challenging, particularly in achieving reliable multi-class classification accuracy.To address this challenge, this study proposes an advanced feature extraction algorithm to enhance MI-BCI performance using a custom-built five-class MI-EEG dataset. The proposed method, EHT-CSP, integrates Ensemble Empirical Mode Decomposition Hilbert-Huang Transform (EEMD-HHT) with Time-Frequency Common Spatial Pattern (TFCSP). Specifically, it extracts marginal spectrum entropy and energy spectrum entropy via EEMD-HHT. It then combines these features with TFCSP-derived feature vectors to improve feature discrimination. The Light Gradient Boosting Machine is then employed for classification. The proposed MI-BCI system is evaluated through both offline analysis and real-world BCW obstacle avoidance experiments. Results demonstrate that the algorithm achieves an average classification accuracy of 78.45%, with all participants successfully completing BCW navigation tasks. In this study, LightGBM and EHT-CSP are compared with other algorithms respectively, and it is verified that the proposed model is superior to the existing models.},
}

@article {pmid41647137,
year = {2026},
author = {Gong, A and Man, H and Shi, X and Li, S and Hu, X and Gong, B and Shi, T and Fu, Y},
title = {Trading time for space: a new approach to investigate the EEG neural mechanisms of fine motor brain based on ICA-optimized traceability network analysis.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {35},
pmid = {41647137},
issn = {1871-4080},
abstract = {UNLABELLED: Although electroencephalography (EEG) offers significant advantages in terms of high temporal resolution and cost-effectiveness, its application is often constrained by limited spatial resolution. This limitation makes it challenging to accurately localize and characterize activity within specific target regions of the brain. To address this, we propose a computational model for brain-network analysis based on independent component analysis (ICA) and source-space clustering. First, repetitive ICA decomposition is performed on a trial-by-trial basis, followed by clustering to extract stable independent components and their corresponding spatial mapping vectors. Subsequently, standardized low-resolution brain electromagnetic tomography (sLORETA) is employed for source localization. The resulting source locations are then clustered across trials to define network nodes, which are utilized to construct a source-level brain network for the investigation of neural mechanisms. The efficacy of this algorithm was validated using two datasets: the international Brain-Computer Interface (BCI) competition dataset involving motor imagery, and a self-collected dataset recorded during the preparatory phase of pistol shooting. Analysis of the motor-imagery dataset demonstrated that the proposed method identified active brain regions consistent with those observed in previous functional magnetic resonance imaging (fMRI) studies. Regarding the pistol-shooting preparation dataset, the method revealed heightened activity in the frontal, occipital, and bilateral temporal lobes. Furthermore, the intensity of information interaction among multiple brain regions exhibited a significant correlation with shooting performance. These findings not only corroborate prior research but also uncover novel features regarding source-level functional connectivity. Consequently, this novel framework achieves precise source localization and network analysis using EEG, significantly enhancing spatial resolution and providing a more accurate elucidation of target brain activities and information-interaction mechanisms during motor tasks.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10405-z.},
}

@article {pmid41647136,
year = {2026},
author = {Guo, K and Meng, K and Yu, R and Zhang, L and Hu, Y and Zhang, R and Yao, D and Chen, M},
title = {State-dependent alterations of network characteristics between seizure and non-seizure onset zones in drug-resistant epilepsy.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {31},
pmid = {41647136},
issn = {1871-4080},
abstract = {UNLABELLED: Accurate localization of the seizure onset zone (SOZ) is critical for successful surgery in drug-resistant epilepsy (DRE). To investigate the alterations of network characteristics between the SOZ and non-seizure onset zones (NSOZ) across different seizure stages, the intracranial electroencephalogram (iEEG) data based brain networks from 29 DRE patients have been constructed using the weighted phase lag index (WPLI) and phase transfer entropy (PTE), respectively. Then, graph theory metrics, such as eigenvector centrality, betweenness centrality, in-degree and out-degree, are calculated to compare network characteristics of SOZ and NSOZ nodes across interictal, pre-ictal, early-ictal and post-ictal periods in multiple frequency bands. Statistical analyses demonstrate that the SOZ exhibits significantly higher eigenvector centrality and betweenness centrality in the beta and gamma frequency bands, serving as network hubs and primary sources of information outflow. By contrast, the NSOZ shows elevated centrality only in the theta and alpha frequency bands during non-ictal states. Moreover, during the pre-ictal to early-ictal transition, the SOZ progressively evolves into hub nodes with enhanced outflow and reduced inflow, whereas the NSOZ shifts toward non-hub status with increased inflow. Importantly, the random forest model utilizing out-degree features of early-ictal gamma frequency band can effectively identify the SOZ, and achieve an area under the curve (AUC) of 0.82. Overall, these findings offer a novel network-based perspective on the state-dependent alterations of epileptic seizures in DRE and contribute to the treatment of epilepsy.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10400-4.},
}

@article {pmid41647134,
year = {2026},
author = {Yang, Z and Wang, K and Ming, Y and Yang, H and Chen, Q and Peng, Y and Kong, W},
title = {Uncertainty-aware human-machine collaboration in Camouflaged Object Detection.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {45},
pmid = {41647134},
issn = {1871-4080},
abstract = {Camouflaged Object Detection (COD), the task of identifying objects concealed within their environments, has seen rapid growth due to its wide range of practical applications. We propose a human-machine collaboration framework for COD, leveraging the complementary strengths of computer vision (CV) models and noninvasive brain-computer interfaces (BCIs). Our approach introduces a multiview backbone to estimate uncertainty in CV predictions, utilizes this uncertainty during training to improve efficiency, and defers low-confidence cases to human evaluation via RSVP-based BCIs during testing for more reliable decision-making. Evaluated on the CAMO dataset, our framework achieves state-of-the-art results with an average improvement of 4.56% in balanced accuracy (BA) and 3.66% in the F1 score. For the best-performing participants, improvements reached 7.6% in BA and 6.66% in the F1 score. Training analysis showed a strong correlation between confidence and precision, while ablation studies confirmed the effectiveness of our training policy and human-machine collaboration strategy. This work reduces human cognitive load, improves system reliability, and provides a foundation for advancements in real-world COD applications and human-computer interaction. Our code and data are available at: https://github.com/ziyuey/Uncertainty-aware-human-machine-collaboration-in-camouflaged-object-identification.},
}

@article {pmid41646629,
year = {2026},
author = {Colli, A and Zilla, P and Calafiore, AM and Padalino, M and Naso, F and George, I},
title = {Quantification of Alpha-Gal Expression in Commercial BioProsthetic Heart Valves and Its Potential Mitigation.},
journal = {Structural heart : the journal of the Heart Team},
volume = {10},
number = {3},
pages = {100739},
pmid = {41646629},
issn = {2474-8714},
abstract = {BACKGROUND AND AIMS: Bioprosthetic heart valves (BHVs) are inherently susceptible to structural degeneration, driven by a combination of mechanical stress, lipid infiltration, glutaraldehyde-induced crosslinking instability, and progressive calcification. Recent evidence has implicated the αGal antigen (galactose-α-1,3-galactose) as an additional contributor to BHV deterioration through activation of innate immune pathways. The present study aims to: 1) perform a quantitative assessment of the residual presence of xenoantigens, specifically αGal, in a range of commercial BHV models; 2) evaluate the efficacy of an experimental polyphenol-based treatment in neutralizing these antigenic determinants; and 3) investigate the long-term stability of glutaraldehyde fixation concerning the potential re-exposure of αGal epitopes.

METHODS: Twelve distinct BHV models were subjected to in vitro analysis for αGal antigen quantification both before and following application of an experimental polyphenol treatment. Additionally, glutaraldehyde-fixed bovine pericardial tissues were incubated in a physiologically mimetic, blood-like environment for up to 9 years in real-time to simulate the long-term behavior of BHV materials and assess antigen unmasking associated with glutaraldehyde degradation.

RESULTS: The average count of the αGal epitope in original pericardial valve models was 4.18 ± 0.72 × 10[11]/10 mg of tissue, whereas porcine valve-derived prostheses exhibited a higher mean value of 8.51 ± 2.17 × 10[11]/10 mg. Treatment with the polyphenol formulation resulted in a marked reduction (approximately 99%) in detectable αGal epitopes. Furthermore, glutaraldehyde fixed pericardial tissues subjected to prolonged incubation demonstrated up to 60% re-exposure of previously masked αGal antigens after 9 years, consistent with a progressive compromise of glutaraldehyde crosslinking integrity.

CONCLUSION: The data confirm that commercially available BHVs retain a substantial immunogenic burden attributable to αGal xenoantigens. Importantly, the overtime degradation of glutaraldehyde crosslinks facilitates the gradual re-exhibition of these epitopes, potentially undermining long-term valve performance. The pronounced efficacy of polyphenol-based treatment in inhibiting αGal antigens highlights its promise as a biocompatibility-enhancing pretreatment strategy for next-generation BHVs.},
}

@article {pmid41643590,
year = {2026},
author = {Sehnan, M and Li, H and Li, X and Grebogi, C and Gao, Z and Dang, W},
title = {Multiscale spatiotemporal neural network with multi-attention mechanism using brain partitioning for motor imagery recognition.},
journal = {Journal of neuroscience methods},
volume = {429},
number = {},
pages = {110704},
doi = {10.1016/j.jneumeth.2026.110704},
pmid = {41643590},
issn = {1872-678X},
abstract = {BACKGROUND: Motor imagery (MI)-based electroencephalogram (EEG) brain-computer interfaces (BCIs) facilitate communication for motor-impaired patients by leveraging artificial intelligence to accurately interpret brain signals. However, EEG signal classification remains challenging due to low signal-to-noise ratio (SNR) and individual variability in brain activity.

NEW METHOD: We propose a novel parallel multi-depth spatial-temporal neural network aimed at enhancing the integration of spatial and temporal features from multichannel EEG signals by leveraging brain functional topography. To improve cortical representations associated with motor imagery, the model incorporates two parallel branches. One branch focuses on inter-channel differences corresponding to contralateral electrode pairs, emphasizing hemispheric disparities, while the other targets the frontal and parietal brain regions. These region-specific enhanced signal representations are then fed into the multi-depth spatial-temporal network for feature extraction and subsequent motor imagery classification. The architecture of the feature extraction network integrates four specialized blocks, ensuring the comprehensive capture of discriminative features that are particularly sensitive to task-relevant frequencies for each MI class. A multi-loss design further optimizes feature integration across networks.

RESULTS: Cross-validation results on the BCI Competition IV 2a dataset and High Gamma dataset achieve accuracies of 82.14% and 95.61%, respectively, with kappa values of 0.76 and 0.93, surpassing state-of-the-art methods.

CONCLUSION: These experimental results highlight the significance of parallel spatial-temporal networks based on brain partitioning for MI classification in rehabilitation engineering and real-world BCI applications.},
}

@article {pmid41642225,
year = {2026},
author = {R, V and Robinson, N and Reddy, MR},
title = {Enhancing the performance of a deep convolutional neural network model for motor imagery classification using EEG channel-wise attention module.},
journal = {Medical engineering & physics},
volume = {147},
number = {1},
pages = {},
doi = {10.1088/1873-4030/ae1afe},
pmid = {41642225},
issn = {1873-4030},
mesh = {*Electroencephalography ; Humans ; *Neural Networks, Computer ; *Attention ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; *Imagination ; Convolutional Neural Networks ; },
abstract = {The classification of motor imagery-electroencephalography (MI-EEG) is a growing research field in brain-computer interface, which allows people with motor disabilities to communicate with the outside world through assistive devices. Although deep learning-based models have revolutionized MI-EEG decoding, dealing with the MI-EEG signals remains challenging due to the signals being non-stationary, containing noisy signals, and having a low signal-to-noise-ratio. This study proposes to employ a novel EEG channel-wise attention module (ECWAM) in a deep convolutional neural network (deep CNN) to enhance the accuracy of MI-EEG decoding. The proposed method calculates the channel score for each mu band EEG channel and amplifies the prominent EEG channels based on their channel scores. The proposed method is evaluated on 54 subjects, binary class MI dataset from the Korea University EEG dataset. Additionally, the proposed method is compared with the conventional channel-wise attention module mentioned in the literature. The results for the hold-out analysis outcomes suggest that the proposed deep CNN with ECWAM has statistically improved the average classification accuracy of the baseline deep CNN model from 63.96% to 68.98%, withp-value = 0.02 for the subject-specific MI classification. Further, the scalp map of the EEG channel ranking obtained by the proposed method and the conventional channel-wise attention module mentioned in the literature is also compared. The results of the comparison show that the proposed method yields a higher channel ranking in the brain's motor cortex region, which is the primary contributing area for MI activity.},
}

*Electroencephalography
Humans
*Neural Networks, Computer
*Attention
*Signal Processing, Computer-Assisted
Brain-Computer Interfaces
*Imagination
Convolutional Neural Networks

@article {pmid41641333,
year = {2026},
author = {Lin, Y and Yuan, Y and Chen, J and Lin, X},
title = {Motor imagery combined with brain-computer interface for stroke patients: a meta-analysis.},
journal = {Frontiers in neurology},
volume = {17},
number = {},
pages = {1672882},
pmid = {41641333},
issn = {1664-2295},
abstract = {OBJECTIVE: To systematically evaluate the effects of motor imagery combined with brain-computer interface (MI-BCI) on stroke patients.

METHODS: Randomized controlled trials (RCTs) on MI-BCI for stroke patients were retrieved from CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, Embase, and Web of Science databases from inception to June 2025. Data were analyzed using RevMan 5.2 software.

RESULTS: Eight RCTs involving 357 stroke patients were included. The meta-analysis showed that MI-BCI was associated with an improvement in upper limb motor function, although this did not reach conventional statistical significance (SMD = 0.86, 95% CI = -0.04 to 1.75, p = 0.06). In contrast, a statistically significant, moderate-to-large improvement was found in activities of daily living (SMD = 1.47, 95% CI = 0.51 to 2.44, p = 0.003). Subgroup analyses indicated that the efficacy in motor function was primarily evident when MI-BCI was administered as an adjunct to conventional rehabilitation or with an intervention duration of ≥4 weeks.

CONCLUSION: The efficacy of MI-BCI is contingent upon its therapeutic context. When used as an adjunct to conventional rehabilitation, MI-BCI can significantly improve both upper limb motor function and activities of daily living in stroke patients. However, current evidence does not support its superiority over motor imagery alone when applied as a standalone therapy. An intervention duration of ≥4 weeks is recommended to achieve significant functional gains.},
}

@article {pmid41640392,
year = {2026},
author = {E, M and Li, X and Zhang, Y and Yin, J and Mayo, KH and Wang, H},
title = {Simple Sequence Repeat Gene Polymorphisms in Yellow-Rumped Flycatcher With Gender-Specific Associations and Personality Variations.},
journal = {Ecology and evolution},
volume = {16},
number = {2},
pages = {e72991},
pmid = {41640392},
issn = {2045-7758},
abstract = {This study explores the genetic and physiological facets of personality variations in the yellow-rumped flycatcher (Ficedula zanthopygia), with a focus on potential sex-specific associations between simple sequence repeat (SSR) polymorphisms, body condition index (BCI) and behavioral traits. During the 2020 breeding seasons at Zuojia Nature Reserve, northeast China, we conducted field investigations using several stress tests to quantify personality as reflected in breathing rates. This metric demonstrated significant reproducibility between life stages, thereby validating its use as a reliable association with individual boldness. We further examined the influence of genetic diversity by genotyping 10 highly polymorphic SSR loci and calculating individual heterozygosity. As a reflection of stronger personalities, we found significant associations between individual heterozygosity and breathing rates in female adults, with greater heterozygosity correlated with lower breathing rates. The opposite pattern was observed in male nestlings, and no significant correlations were observed in male adults or male chicks. In addition, the BCI tended to be negatively correlated with breathing rates in female adults, suggesting that individuals with better body conditions were less fearful. These findings underscore the importance of genetic diversity and body condition in modulating personality traits, particularly in females. Overall, our results highlight the likelihood that the sex of these birds underlies their behavioral variations. Moreover, this study provides insight into the genetic basis of personality in cavity-nesting birds and emphasizes the need for further research to elucidate specific genetic pathways that influence these traits.},
}

@article {pmid41639858,
year = {2026},
author = {Schopp, L and Starke, G and Ienca, M},
title = {Explainability in AI-enabled medical neurotechnology: a scoping review.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-026-01892-0},
pmid = {41639858},
issn = {1743-0003},
}

@article {pmid41638413,
year = {2026},
author = {Niu, R and Li, Y and Liu, L and Pan, Y and Liu, Y},
title = {Hierarchical neurobehavioral model reveals that shared flexibility, not individual stability, supports rhythmic coordination.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121773},
doi = {10.1016/j.neuroimage.2026.121773},
pmid = {41638413},
issn = {1095-9572},
abstract = {Interpersonal coordination requires balancing individual control with interaction-derived synergy, yet it remains unclear when neural coupling contributes beyond behavior. Using an fNIRS hyperscanning paradigm, we examined dyadic rhythmic coordination and jointly modeled behavioral stability, dispositional structure, and interbrain synchrony within a hierarchical neurobehavioral framework. Across models, mean individual stability was negatively associated with dyadic performance, whereas interaction-derived behavioral synergy was the most robust positive predictor. Incorporating dispositional structure showed that larger within-dyad differences in figure-embedding performance impaired coordination, whereas higher dyad-level self-esteem facilitated coordination. The neural coupling index (NCI) showed no reliable main effect after accounting for behavioral and trait factors, but moderation analyses indicated a conditional contribution: interbrain synchrony compensated when behavioral synergy was low, with diminishing benefit as synergy increased. Together, these findings support a hierarchical neurobehavioral architecture in which behavioral synergy provides the primary foundation of coordination, dispositional structure shapes the conditions for synergy, and interbrain synchrony contributes in a context-dependent manner.},
}

@article {pmid41637791,
year = {2026},
author = {O'Regan, RM and Ren, Y and Zhang, Y and Fleming, GF and Francis, PA and Pagani, O and Walley, BA and Kammler, R and Dell'Orto, P and Viale, G and Loi, S and Colleoni, M and Treuner, K and Regan, MM},
title = {Identifying premenopausal patients with early-stage hormone receptor-positive breast cancer at minimal risk of distant recurrence by breast cancer index.},
journal = {Breast (Edinburgh, Scotland)},
volume = {86},
number = {},
pages = {104714},
doi = {10.1016/j.breast.2026.104714},
pmid = {41637791},
issn = {1532-3080},
abstract = {BACKGROUND: An adjusted Breast Cancer Index (BCI) model with an additional cutpoint identified postmenopausal women with hormone-receptor-positive node-negative disease at minimal (<5%) risk of distant recurrence (DR) within 10 years.

METHODS: 2025 premenopausal patients with hormone-receptor-positive node-negative breast cancer, randomized to adjuvant endocrine therapy in SOFT and TEXT (35.6% and 40.4% received adjuvant chemotherapy, respectively), previously had BCI assessed. The additional BCI cutpoint re-classified a subset of the low-risk group into minimal-risk; those in intermediate- or high-risk groups were unchanged. The 10-year DR was estimated by Kaplan-Meier method.

RESULTS: The adjusted BCI model re-classified 17.8 % and 19.6 % of node-negative disease in SOFT and TEXT into BCI minimal-risk groups; 43.2 % and 38.3 % remained classified in low-risk groups, respectively. In SOFT, the estimated 10-year DR was 2.3 % (95 %CI 0.9-6.0 %) and 4.1 % (95 %CI 2.6-6.5 %) in the minimal-risk and revised low-risk groups, respectively. In TEXT, the estimated 10-year DR was 2.0 % (95 %CI 0.7-6.2 %) and 4.6 % (95 %CI 2.8-7.7 %) in the minimal- and low-risk groups, respectively.

CONCLUSIONS: This study confirmed prognostic ability of the minimal-risk BCI cutpoint to classify patients estimated to have minimal-risk of distant recurrence within 10 years among premenopausal patients treated for hormone-receptor-positive node-negative breast cancer, providing relevant information for personalizing adjuvant endocrine therapy. SOFT: (clinicaltrials.gov NCT00066690) TEXT: (clinicaltrials.gov NCT00066703).},
}

@article {pmid41636698,
year = {2026},
author = {Zhang, B and Yu, Z and Yan, F and Sun, Y and Ye, J and Liu, X and Qi, S and Wei, X and Liu, S and Ming, D},
title = {Altered Salience-Default Mode Network Dynamics in Subclinical Depression: A Preclustering-Based Co-Activation Pattern Analysis.},
journal = {CNS neuroscience & therapeutics},
volume = {32},
number = {2},
pages = {e70736},
pmid = {41636698},
issn = {1755-5949},
support = {2023YFF1203700//National Key Research and Development Program of China/ ; 62376187//National Natural Science Foundation of China/ ; 24HHNJSS00016//Autonomous Project of Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration/ ; },
mesh = {Humans ; Male ; Female ; Magnetic Resonance Imaging/methods ; Adult ; *Default Mode Network/diagnostic imaging/physiopathology ; *Depression/diagnostic imaging/physiopathology ; Young Adult ; *Brain/physiopathology/diagnostic imaging ; Machine Learning ; Middle Aged ; *Nerve Net/diagnostic imaging/physiopathology ; Brain Mapping ; },
abstract = {BACKGROUND: Neuroimaging studies frequently report aberrant spontaneous brain activity and functional connectivity within core functional networks, including the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in subclinical depression (SD). However, the dynamic coordination among these networks remains poorly understood, impeding comprehensive elucidation of the underlying neuropathology of SD.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected from subjects with SD (n = 26) and healthy controls (HCs, n = 33). A preclustering-based co-activation pattern method was developed to investigate the dynamic patterns of network coordination. Finally, machine learning analysis was conducted to evaluate the potential of network dynamics for clinical diagnosis.

RESULTS: Subjects with SD exhibited decreased dwell time in the SN and increased transition frequency from the SN to DMN, which was positively correlated with depressive severity. Furthermore, an ensemble learning model based on SN-DMN dynamic features achieved a classification accuracy of 96.44% in distinguishing SD from HC.

CONCLUSION: These findings underscore the potential of altered SN-DMN dynamics as candidates for future neuroimaging markers of SD and support a neurocognitive model whereby altered SN-DMN dynamic coordination makes subjects with SD more prone to internal directed attention biases, thereby contributing to self-related depressive symptoms like rumination.},
}

Humans
Male
Female
Magnetic Resonance Imaging/methods
Adult
*Default Mode Network/diagnostic imaging/physiopathology
*Depression/diagnostic imaging/physiopathology
Young Adult
*Brain/physiopathology/diagnostic imaging
Machine Learning
Middle Aged
*Nerve Net/diagnostic imaging/physiopathology
Brain Mapping

@article {pmid41635520,
year = {2025},
author = {Liu, Y and Xu, P and Hu, S},
title = {Resting-state gamma power in schizophrenia: a systematic review and meta-analysis.},
journal = {Frontiers in psychiatry},
volume = {16},
number = {},
pages = {1731645},
pmid = {41635520},
issn = {1664-0640},
abstract = {Gamma-band oscillations, generated by excitatory-inhibitory circuit interactions, are strongly implicated in schizophrenia, yet evidence on resting-state abnormalities remains inconsistent. We conducted a systematic review and meta-analysis of EEG and MEG studies comparing resting-state gamma activity in patients with schizophrenia and healthy controls, following PRISMA guidelines and assessing study quality with the Newcastle-Ottawa Scale. Twenty studies (n = 998 patients; n = 952 controls) were included. Standardized mean differences (Hedges' g) were calculated and pooled using random-effects models. Results demonstrated a significant elevation of whole-brain gamma power in schizophrenia (g=0.371; 95% CI = 0.119-0.622; P < 0.001; I[2] = 78.2%). Region-specific analyses showed increases in frontal and temporal cortices, with smaller or inconsistent effects in parietal, occipital, and default mode network (DMN) regions. Meta-regression revealed illness duration (β=1.13) and medication status (β=0.43) as positive predictors, while eyes-open resting conditions attenuated effects (β=-0.70), indicating that both clinical chronicity and methodological factors contribute to heterogeneity. Publication bias was not evident by Egger's test, although trim-and-fill suggested five potentially missing small-effect studies, reducing the pooled estimate to g=0.130. Sensitivity analyses confirmed that findings were not driven by outliers, and GRADE assessments rated the certainty of evidence as moderate for whole-brain gamma and low for regional outcomes. Taken together, these findings suggest that resting-state gamma power differences in schizophrenia represent a small and heterogeneous group-level effect, shaped by illness duration, medication status, and recording conditions. Rather than indicating a uniform abnormality, the results underscore substantial variability across studies and highlight the need for cautious interpretation. Future large-scale, longitudinal, and multimodal investigations-particularly in unmedicated and first-episode patients-are warranted to clarify the temporal dynamics, causal mechanisms, and potential translational relevance of resting-state gamma activity in schizophrenia.},
}

@article {pmid41632971,
year = {2026},
author = {Li, YY and Hu, AQ and Yi, LL and Mao, ZX and Lü, QY and Wang, J and Wei, W and Huang, YQ and Huang, S and Dai, WJ and Qiao, MX and Xu, JJ and Wang, Q and Li, XJ and Luo, FG and Deng, W and Hu, YZ and Li, T and Guo, WJ},
title = {Comparing the Associations of Internet Addiction and Internet Gaming Disorder With Psychopathological Symptoms: Cross-Sectional Study of Three Independent Adolescent Samples.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e82414},
doi = {10.2196/82414},
pmid = {41632971},
issn = {1438-8871},
mesh = {Humans ; Cross-Sectional Studies ; Adolescent ; *Internet Addiction Disorder/psychology/epidemiology ; Male ; Female ; Young Adult ; *Video Games/psychology ; *Internet ; Surveys and Questionnaires ; *Behavior, Addictive/psychology/epidemiology ; China/epidemiology ; },
abstract = {BACKGROUND: Both internet gaming disorder (IGD) and internet addiction (IA) have been associated with diverse psychopathological symptoms. However, how the 2 conditions relate to each other and which is more strongly associated with psychopathology remain unclear.

OBJECTIVE: This study aimed to examine the association between IGD and IA and compare the strength of their associations with various types of psychopathological symptoms.

METHODS: This cross-sectional study surveyed 3 independent samples of Chinese adolescents: the first sample (S1) comprised 8194 first-year undergraduates at a comprehensive university in Chengdu, the second sample (S2) comprised 1720 students from a high school in Hangzhou, and the third sample (S3) comprised 551 inpatients aged 13 to 19 years recruited from 2 tertiary psychiatric hospitals in Hangzhou and Chengdu. IGD was defined as a score of 22 or more on the Internet Gaming Disorder Scale-Short Form (IGDS9-SF), whereas IA was defined as a score of 50 or more on Young's 20-item Internet Addiction Test (IAT-20). Symptoms of depression, anxiety, psychoticism, paranoid ideation, and attention-deficit or hyperactivity were assessed using internationally validated scales including 9-item the Patient Health Questionnaire, 7-item Generalized Anxiety Disorder, psychoticism and paranoid ideation subscales of the Symptom Checklist 90 (absent for S2), and Adult ADHD Self-Report Scale (absent for S1), through online surveys in S1 (October 2020) and S3 (January 2022 to February 2025) and via an offline survey in S2 (March 2024).

RESULTS: The prevalence estimates (95% CI) of IGD were 4.8% (4.3%-5.2%) in S1, 15.8% (14.0%-17.5%) in S2, and 32.3% (28.4%-36.2%) in S3, whereas prevalence estimates (95% CI) of IA were consistently higher across samples, ranging from 7.3% (6.8%-7.9%) in S1 and 18.8% (17.0%-20.6%) in S2 to 45.9% (41.8%-50.1%) in S3. The IGDS9-SF and the IAT-20 were moderately correlated (Pearson r=0.51-0.57; all P<.001) and were associated with the severity of most psychopathological symptom domains, with consistently stronger associations observed for IAT-20 scores. In multivariate models including all psychopathological symptoms as independent variables, the coefficients of determination (R², 95% CIs) were consistently higher for the IAT-20 than for the IGDS9-SF in S1 (0.33, 0.30-0.35 vs 0.13, 0.11-0.16) and S2 (0.44, 0.39-0.49 vs 0.23, 0.18-0.27), with a similar but nonsignificant pattern observed in S3 (0.13, 0.06-0.26 vs 0.06, 0.03-0.16). Post hoc analyses indicated that psychopathological symptoms were generally more severe in individuals with IA, either alone or comorbid with IGD, than in those with IGD only.

CONCLUSIONS: This study provides additional evidence that IGD and IA are distinct yet interrelated constructs, and further demonstrates that IA consistently exhibits stronger associations with the severity of psychopathological symptoms than IGD. These findings underscore the importance of recognizing and addressing compulsive and problematic online behaviors that extend beyond gaming, highlighting the need to refine diagnostic frameworks and prioritize targeted clinical interventions.},
}

Humans
Cross-Sectional Studies
Adolescent
*Internet Addiction Disorder/psychology/epidemiology
Male
Female
Young Adult
*Video Games/psychology
*Internet
Surveys and Questionnaires
*Behavior, Addictive/psychology/epidemiology
China/epidemiology

@article {pmid41632672,
year = {2026},
author = {Xu, Y and Vong, CM and Xu, Z and Fu, J and Li, J and Chen, C},
title = {Disentangled Multimodal Spatiotemporal Learning for Hybrid EEG-fNIRS Brain-Computer Interface.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3660692},
pmid = {41632672},
issn = {1558-2531},
abstract = {The hybrid EEG-fNIRS Brain-computer interface (BCI) combines the high temporal resolution of electroencephalography (EEG) with the high spatial resolution of functional near-infrared spectroscopy (fNIRS) to enable comprehensive brain activity detection. However, integrating these modalities to obtain highly discriminative features remains challenging. Most existing methods fail to effectively capture the spatiotemporal coupling features and correlations between EEG and fNIRS signals. Furthermore, these methods adopt a holistic learning paradigm for the representation of each modality, leading to unrefined and redundant multimodal representations. To address these challenges, we propose a disentangled multimodal spatiotemporal learning (DMSL) method for hybrid EEG-fNIRS BCI systems, which simultaneously performs multimodal spatiotemporal coupling and disentangled representation learning within a unified framework. Specifically, DMSL utilizes a compact convolutional module with one-dimensional temporal and spatial convolution layers to extract complex spatiotemporal patterns from each modality and introduces a multimodal attention interaction module to comprehensively capture the inter-modality correlations, enhancing the representations for each modality. Subsequently, DMSL designs an adaptive multi-branch graph convolutional module based on reconstructed channels to effectively capture the spatiotemporal coupling features, incorporating modality consistency and disparity constraints to disentangle common and modality-specific representations for each modality. These disentangled representations are finally adaptively fused to perform different task predictions. The proposed DMSL demonstrates state-of-the-art performance on publicly available datasets for mental arithmetic, motor imagery, and emotion recognition tasks, exceeding the best baselines by 2.34%, 0.59%, and 1.47%, respectively. These results demonstrate the effectiveness of DMSL in improving EEG-fNIRS decoding and its strong generalization ability in BCI applications.},
}

@article {pmid41631479,
year = {2026},
author = {Leung, J and Holanda, LJ and Wheeler, L and Chau, T},
title = {Wireless in-ear EEG system for auditory brain-computer interface applications in adolescents.},
journal = {Biomedical physics & engineering express},
volume = {12},
number = {1},
pages = {},
doi = {10.1088/2057-1976/ae3b45},
pmid = {41631479},
issn = {2057-1976},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/instrumentation/methods ; Adolescent ; Male ; Female ; *Wireless Technology/instrumentation ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Artifacts ; *Ear ; Acoustic Stimulation ; },
abstract = {In-ear electroencephalography (EEG) systems offer several practical advantages over scalp-based EEG systems for non-invasive brain-computer interface (BCI) applications. However, the difficulty in fabricating in-ear EEG systems can limit their accessibility for BCI use cases. In this study, we developed a portable, low-cost wireless in-ear EEG device using commercially available components. In-ear EEG signals (referenced to left mastoid) from 5 adolescent participants were compared to scalp-EEG collected simultaneously during an alpha modulation task, various artifact induction tasks, and an auditory word-streaming BCI paradigm. Spectral analysis confirmed that the proposed in-ear EEG system could capture significantly increased alpha activity during eyes-closed relaxation in 3 of 5 participants, with a signal-to-noise ratio of 2.34 across all participants. In-ear EEG signals were most susceptible to horizontal head movement, coughing and vocalization artifacts but were relatively insensitive to ocular artifacts such as blinking. For the auditory streaming paradigm, the classifier decoded the presented stimuli from in-ear EEG signals only in 1 of 5 participants. Classification of the attended stream did not exceed chance levels. Contrast plots showing the difference between attended and unattended streams revealed reduced amplitudes of in-ear EEG responses relative to scalp-EEG responses. Hardware modifications are needed to amplify in-ear signals and measure electrode-skin impedances to improve the viability of in-ear EEG for BCI applications.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/instrumentation/methods
Adolescent
Male
Female
*Wireless Technology/instrumentation
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Artifacts
*Ear
Acoustic Stimulation

@article {pmid41629357,
year = {2026},
author = {Wang, F and Chen, Y and Wang, P and Gong, A and Xu, J and Fu, Y},
title = {An EEG dataset for handwriting imagery decoding of Chinese character strokes and Pinyin single vowels.},
journal = {Scientific data},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41597-026-06708-3},
pmid = {41629357},
issn = {2052-4463},
support = {Grant Nos. 62376112, 82172058, 81771926, 61763022, 62366026, and 62006246//National Natural Science Foundation of China (National Science Foundation of China)/ ; Grant No. 2023M734315//China Postdoctoral Science Foundation/ ; },
abstract = {Non-invasive EEG-based brain-computer interfaces (BCI) for handwriting imagery can support the restoration of fine writing abilities in individuals with motor impairments. However, the development of high-performance decoding algorithms is constrained by scarce training datasets. To address this, we present the first open EEG dataset dedicated to handwriting imagery. The dataset comprises 32-channel EEG recordings (sampled at 1000 Hz) from 21 healthy participants across two sessions separated by at least 24 hours. A dual-paradigm design captures multidimensional neural features: a Chinese character stroke imagery task (five basic strokes, 200 trials per session) and a Pinyin single-vowel imagery task (six vowels, 240 trials per session). After rigorous quality screening, 18,480 standardized trials are provided, ensuring completeness, reliability, and adherence to the Brain Imaging Data Structure (BIDS) standard. This dataset enables the development and evaluation of algorithms for non-invasive BCI and supports research on restoring writing-based communication in individuals with motor impairments.},
}

@article {pmid41626718,
year = {2026},
author = {Saeed, S and Luo, Z and Wang, H and Xu, L and Zhang, X and Tang, A and Ma, X and Lai, J and Song, P and Hu, S},
title = {Mapping the Global Burden and Inequalities of Bipolar Disorder, 1990-2021, With Projections to 2050: A Systematic Analysis.},
journal = {Bipolar disorders},
volume = {28},
number = {1},
pages = {e70074},
doi = {10.1111/bdi.70074},
pmid = {41626718},
issn = {1399-5618},
support = {2023YFC2506200//National Key Research and Development Program of China/ ; JNL-2023001B//The Research Project of Jinan Microecological Biomedicine Shandong Laboratory/ ; 2021C03107//The Zhejiang Provincial Key Research and Development Program/ ; 2021R52016//The Leading Talent of Scientific and Technological Innovation-"Ten Thousand Talents Program" of Zhejiang Province/ ; 2020R01001//The Innovation team for precision diagnosis and treatment of major brain diseases/ ; 2022KTZ004//Chinese Medical Education Association/ ; },
mesh = {Humans ; *Bipolar Disorder/epidemiology ; Male ; Female ; *Global Health/statistics & numerical data ; Adult ; Middle Aged ; *Global Burden of Disease/trends ; Incidence ; Prevalence ; Young Adult ; Socioeconomic Factors ; Adolescent ; Aged ; Sex Factors ; },
abstract = {BACKGROUND: Bipolar disorder is a severe mental disorder affecting millions worldwide, necessitating comprehensive policies and interventions.

AIMS: To provide assessment of global inequalities in the burden of bipolar disorder and their projected trajectories to 2050.

METHODS: Global Burden of Disease 2021 data from 204 countries and territories were analyzed, stratified by age, gender, and Socio-demographic Index (SDI) quintiles. Age-standardized prevalence (ASPR), incidence (ASIR), and years lived with disability (ASR YLD) per 100,000 population were calculated. Inequalities were assessed using the slope index of inequality (SII) and concentration index (CI), and ARIMA models were applied to project trends to 2050.

RESULTS: From 1990 to 2021, global incidence of BD increased, while prevalence and years lived with disability (YLDs) remained relatively stable (ASPR: 453.7 [95% UI: 381.6-540.8] to 454.6 [95% UI: 377.9-545.8]). Females consistently had higher prevalence than males (474.2 vs. 435.0 per 100,000 in 2021). High-SDI regions reported the highest rates, with Australasia reaching 1110.8 (95% UI: 940.3-1305.9). The SII for incidence rose slightly (10.87-11.38), while the CI declined (0.096-0.012), indicating increasing absolute but decreasing relative inequalities. Projections suggest a rising global burden, with female prevalence remaining higher and incidence rates converging between genders (global ASIR: 33.8 per 100,000).

CONCLUSION: Global inequalities in bipolar disorder persist, disproportionately affecting females and high-SDI regions. Projected trends indicate an increasing burden with a narrowing gender gap in incidence, emphasizing the need for targeted interventions and further research on long-term impacts, including the effects of COVID-19.},
}

Humans
*Bipolar Disorder/epidemiology
Male
Female
*Global Health/statistics & numerical data
Adult
Middle Aged
*Global Burden of Disease/trends
Incidence
Prevalence
Young Adult
Socioeconomic Factors
Adolescent
Aged
Sex Factors

@article {pmid41625625,
year = {2025},
author = {Muhsin, SM and Akbar, MA and Mustari, S and Alashaikh, MH and Chin, AHB},
title = {Human cognitive enhancement and reprogenetic technologies in Malaysia - A survey study of local Muslim undergraduate students' viewpoints.},
journal = {Frontiers in sociology},
volume = {10},
number = {},
pages = {1701007},
pmid = {41625625},
issn = {2297-7775},
abstract = {INTRODUCTION: Newly emerging human enhancement technologies such as brain chip implants, CRISPR-Cas9-based gene editing, and polygenic embryo screening (PES) alongside preimplantation genetic testing (PGT-P) are highly controversial in Islam. However, the prevailing sociocultural dynamics encourage their uptake. In the current era of declining fertility rates, increased parental investment in fewer children has resulted in a flourishing tuition industry, accompanied by heightened academic pressure on students and widespread parental anxiety. These emerging technologies can be employed for cognitive enhancement, thereby providing an expedient solution for parents and students navigating a highly competitive educational environment.

MATERIALS AND METHODS: To inform and facilitate future policy decision-making, an online survey was conducted among 575 undergraduate Muslim students at the International Islamic University Malaysia (IIUM) to assess their perspectives and opinions regarding these newly emerging technologies.

RESULTS: The findings indicated a significant level of opposition among respondents to the uptake of human enhancement technologies, with 54.8% opposing polygenic embryo screening, 69.2% opposing gene editing, and 75.3% opposing brain chip implants, reflecting substantial concerns about altering natural human attributes. The results also indicate that numerous Muslim respondents believe that Allah created humans flawlessly and purposefully, asserting that humanity lacks the authority to alter or amend this creation.

DISCUSSION/CONCLUSION: A three-pronged governance approach for human enhancement technologies is thus proposed, which encompasses (i) bioethical safeguards, (ii) public engagement and education, and (iii) economic accessibility. It is suggested that the Malaysian government should actively consult relevant stakeholders and various segments of the public before enacting future legislation on these technologies.},
}

@article {pmid41626035,
year = {2025},
author = {Jackson, MC and Azarraga, RB and Fraix, MP and Agrawal, DK},
title = {Stage-Based Communication Rehabilitation in Amyotrophic Lateral Sclerosis (ALS): A Review of Strategies for Enhancing Quality of Life.},
journal = {Archives of internal medicine research},
volume = {8},
number = {4},
pages = {359-371},
pmid = {41626035},
issn = {2688-5654},
support = {R25 AI179582/AI/NIAID NIH HHS/United States ; },
abstract = {Amyotrophic Lateral Sclerosis (ALS) is an incurable progressive degenerative neuromuscular disease. One way ALS affects patients is through dysarthria significantly impacting a patient's quality of life by affecting their ability to communicate. This makes maintaining relationships, identity and autonomy difficult, all of which affect psychological wellbeing - a determinant of the quality of life. Dysarthria makes communication difficult, and because the regions affected by ALS first are different for each patient, creating strategies for rehabilitating communication can be challenging. In this review we explore the different communication rehabilitation options available and organize them based on if they are usable based on the onset of intelligibility and locked in state. Interventions before the onset of intelligibility in the early stage are proactive measures such as voice banking and education which empower patient autonomy and a sense of control. Interventions between onset of intelligibility and the locked-in state in the middle stage are alternative and augmentative communication strategies varied in accessibility and usability in patients based on their preferences and functional ability. Late-stage interventions which work after a patient with ALS has entered a locked-in state, are the most technologically advanced alternative and augmentative communication devices and rehabilitate function inaccessible by other methods in this disease stage. While assessing patient values and recommending interventions which meet patient needs is most important in rehabilitation of communication in patient with ALS, using a stage-based approach to evaluate and recommend the treatment of dysarthria and communication rehabilitation will optimize quality of life throughout the progression of disease.},
}

@article {pmid41625473,
year = {2025},
author = {Jin, C and Yang, J and Liang, Z and Qiu, J and Zha, R and Yuan, Z and Shen, Y and Zhang, X},
title = {Navigating online emotion: affective patterns and depressive traits in youth digital engagement.},
journal = {Frontiers in psychology},
volume = {16},
number = {},
pages = {1736426},
pmid = {41625473},
issn = {1664-1078},
abstract = {INTRODUCTION: Youth digital engagement serves as a notable avenue for the expression of emotion and the construction of self among today's youth. This study aims to examine the patterns of youth online emotional expression and their association with individual psychological traits, particularly depressive tendencies.

METHODS: 23,966 Weibo posts published by 103 active youth users were sampled and analyzed. An integrative framework combining Russell's Circumplex Model with multi-level thematic analysis was applied to code each post for valence, arousal, trigger type and coping strategy. Youths also completed a standard depression-screening scale; scores were used to contrast high- versus low-depressive trait sub-groups.

RESULTS: The findings reveal that youth online emotional expression overall is characterized by a self-focused nature, high pleasure, and high arousal. The study also found that individual psychological traits influence emotional expression patterns. Individuals with depressive tendencies showed a significant propensity for higher emotional arousal expression and more no-trigger expression. Furthermore, no-trigger expression plays a mediating role in their emotional expression mechanism.

DISCUSSION: The study provides an integrative framework for youth digital engagement and highlights "no-trigger" expression as a mediator in the framework. These findings can guide early detection efforts and contribute to designing targeted digital mental health supports, as well as informing guidance for families and platform managers.},
}

@article {pmid41624131,
year = {2025},
author = {Guo, X and Li, P and Liu, H and Ding, S},
title = {A systematic review of the effects of brain-computer interface on lower limb motor function, balance function, and activities of daily living in stroke patients.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1641843},
pmid = {41624131},
issn = {1662-4548},
abstract = {OBJECTIVE: To systematically evaluate the effects of brain-computer interface (BCI) technology on lower limb motor function, balance function, and activities of daily living in stroke patients.

METHODS: This study followed the PRISMA guidelines and searched PubMed, Web of Science, EMbase, The Cochrane Library, CNKI, Wanfang, and VIP databases, with an additional manual search. The search period was from database inception to March 2024. The PEDro scale was used to assess the quality of the studies, the GRADE system was applied to evaluate the evidence quality for outcome measures, and Meta-analysis was conducted using Stata 17.0 software.

RESULTS: The systematic review included nine studies. The methodological quality, assessed using the PEDro scale, yielded an average score of 6.9, which corresponds to a moderate-to-low certainty of evidence. The Meta-analysis showed that BCI technology significantly improved lower limb motor function (MD = 3.52, 95% CI [2.03, 5.00], p < 0.001) and activities of daily living (MD = 6.08, 95% CI [1.81, 10.35], p = 0.01), but had no significant effect on balance function (MD = 4.82, 95% CI [-1.53, 11.16], p = 0.14). Subgroup analysis showed that the effect size in the acute and subacute phases was 3.89, and in the recovery phase, it was 3.12, both of which were statistically significant. In terms of intervention methods, the effect size for MI-BCI was 2.73, and for BCI-Robot, it was 4.60, both statistically significant. Regarding intervention dosage, the effect size for 2.5-10 h was 2.60, and for 12-20 h, it was 5.46, both statistically significant.

CONCLUSION: Current evidence suggests that BCI-based interventions have a beneficial effect on lower limb motor function and activities of daily living in stroke patients. Interventions initiated during the acute or subacute phase, with a total dose exceeding 12 h, appear to be associated with superior outcomes. However, the certainty of this evidence is moderate to low, necessitating further validation. Future research should prioritize large-scale, high-quality randomized controlled trials to definitively establish the efficacy of BCI technology and elucidate its optimal implementation protocols.},
}

@article {pmid41623459,
year = {2026},
author = {Eyvazpour, R and Farrokhi, B and Erfanian, A},
title = {A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals.},
journal = {iScience},
volume = {29},
number = {2},
pages = {114521},
pmid = {41623459},
issn = {2589-0042},
abstract = {Decoding continuous 3D hand trajectories from electrocorticographic (ECoG) signals holds potential for brain-computer interface (BCI) applications. However, inter-session variability poses a major challenge for generalization. In this study, we propose a framework that leverages Riemannian-based feature extraction combined with stacked long short-term memory (LSTM) network to enable transfer learning across multiple sessions. ECoG recordings from five monkeys performing reaching tasks are considered. Spatial cross-frequency covariance matrices are computed over the brain area for each of 10 frequency band power and projected onto a Riemannian manifold to extract features which are invariant to session variability. These features and spectral feature are then used to train staked LSTM network. The results show that the proposed method achieves a stable cross-session performance and outperforms baseline models which are trained on frequency features. These findings highlight the potential of combining geometric features with temporal deep learning models for generalized decoding in translational BCI systems.},
}

@article {pmid41623142,
year = {2026},
author = {de Borman, A and Dyck, BV and Rooy, KV and Carrette, E and Meurs, A and Roost, DV and Van Hulle, MM},
title = {Word classification across speech modes from low-density electrocorticography signals.},
journal = {Journal of neural engineering},
volume = {23},
number = {1},
pages = {},
doi = {10.1088/1741-2552/ae3a1b},
pmid = {41623142},
issn = {1741-2552},
mesh = {Humans ; *Electrocorticography/methods ; Male ; *Brain-Computer Interfaces ; Female ; *Speech/physiology ; Adult ; Middle Aged ; Young Adult ; Imagination/physiology ; Speech Perception/physiology ; },
abstract = {Objective.Speech brain-computer interfaces (BCIs) aim to provide an alternative means of communication for individuals who are not able to speak. Remarkable progress has been achieved to decode attempted speech in individuals with severe anarthria. In contrast, imagined speech remains challenging to decode. The underlying neural mechanisms and relations to other speech modes are still elusive.Approach.In this study, we collected low-density electrocorticography signals from ten participants during a word repetition task. Electrodes were implanted for presurgical epilepsy evaluation in participants with preserved speech abilities. Models were developed using linear discriminant analysis to classify five words in response to different speech modes. We compared models trained during speaking, listening, imagining speaking, mouthing and reading. The relations between speech modes were investigated by transferring and augmenting models across speech modes.Main results.As expected, performed speech achieved the highest word classification accuracy followed by listening, mouthing, imagining and reading. While the accuracies obtained were not high enough for practical application, model transfer and augmentation could be investigated across speech modes. Transferring or augmenting models from one speech mode to another mode could significantly improve model performance. In particular, patterns learned from performed and perceived speech could generalize to imagined speech, leading to significantly improved imagined speech performance in seven participants. For four participants, imagined speech could be decoded above chance exclusively when models were transferred or augmented with performed or perceived speech.Significance.Imagined speech is often preferred by speech BCI users over attempted speech, as it requires less effort and can be produced more quickly. Transferring models across speech modes has the potential to facilitate and boost the development of imagined speech decoders.},
}

Humans
*Electrocorticography/methods
Male
*Brain-Computer Interfaces
Female
*Speech/physiology
Adult
Middle Aged
Young Adult
Imagination/physiology
Speech Perception/physiology

@article {pmid41621526,
year = {2026},
author = {Zhao, Y and Zhang, Y and Li, T},
title = {Causal relationships between ADHD, ASD and brain structure: A mendelian randomization study.},
journal = {Progress in neuro-psychopharmacology & biological psychiatry},
volume = {},
number = {},
pages = {111631},
doi = {10.1016/j.pnpbp.2026.111631},
pmid = {41621526},
issn = {1878-4216},
abstract = {Neurodevelopmental disorders (NDDs) are debilitating conditions that impose significant burdens on individuals, families, and society. Despite evidence demonstrated altered brain structure in NDDs, definitive conclusions remain elusive. Using two-sample mendelian randomization (MR) and the latest GWAS findings, the current study aimed to elucidate the causal relationships between grey matter (GM), white matter (WM), subcortical regions, and two prevalent NDDs: attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Our findings identified two frontal regions as key neural substrates in NDDs. Specifically, an increased surface area (SA) of the superior frontal gyrus (SFG) was significantly associated with an enhanced risk of ADHD (P = 2.04E-13, β = 4.28E-02, SE = 5.82E-03), while a larger SA of the orbital frontal gyrus (OFG) was associated with a reduced risk of ASD (P = 1.98E-42, β = -9.8E-02; SE = 0.007). Regarding WM tracts, the mode of anisotropy (MO) in the inferior fronto-occipital fasciculus (IFO) emerged as a causal factor for ADHD (P = 3.36E-70, β = -18.35; SE = 1.04), whereas the MO in the retro-lenticular part of the internal capsule (RLIC) was implicated in ASD (P = 1.37E-04, β = -12.73, SE = 3.34). No reverse causal link, i.e., brain alteration caused by NDDs was identified. Further mediation analyses using functional MRI (fMRI) GWAS data revealed that brain functional activities mediated the relationship between structural brain changes and NDDs risk. In conclusion, our findings underscored the critical role of the frontal lobe and association and projection fibers in the pathophysiology of NDDs, provide novel insights into the neural mechanisms underlying ADHD and ASD.},
}

@article {pmid41621181,
year = {2026},
author = {Spinelli, R and Sanchis, I and de Orellana, M and Humpola, MV and Rietmann, Á and Siano, ÁS},
title = {A nature-inspired peptide from the Boana cordobae frog as a potent and reversible AChE inhibitor with anti-amyloid and neuroprotective activities.},
journal = {Bioorganic chemistry},
volume = {171},
number = {},
pages = {109566},
doi = {10.1016/j.bioorg.2026.109566},
pmid = {41621181},
issn = {1090-2120},
abstract = {Alzheimer's disease (AD) is a multifactorial and progressive neurodegenerative disorder for which no effective treatment currently exists. The development of multitarget-directed ligands (MTDLs) capable of simultaneously modulating several pathological pathways represents a rational strategy to address its complex etiology. In this study, we report the isolation, chemical synthesis, and functional characterization of BcI-4, a short cationic peptide identified from the skin secretion of the Argentinean frog Boana cordobae. The peptide exhibited potent and reversible inhibitory activity against acetylcholinesterase (AChE), with IC50 values of 1.10 and 0.9 μM for recombinant human and Electrophorus electricus AChE, respectively, acting through a non-competitive mechanism involving the peripheral anionic site (PAS). BcI-4 also inhibited AChE-induced β-amyloid (Aβ) aggregation, showed modest monoamine oxidase B (MAO-B) inhibition, and displayed both antioxidant and metal-chelating activities, including inhibition of lipid peroxidation. The peptide retained the multifuctional pharmacological profile previously observed for the crude extract of B. cordobae, with significantly enhanced potency and selectivity toward AChE. Moreover, BcI-4 was non-toxic in vitro (hemolysis and HeLa cell assays) and in vivo (Artemia salina test) even at the highest concentrations tested. Altogether, these findings position BcI-4 as a nature-inspired multitarget peptide with neuroprotective potential, combining reversible AChE inhibition, anti-amyloid, antioxidant, and MAO-B modulatory activities. BcI-4 represents a promising lead compound for the development of peptide-based therapeutics against AD.},
}

@article {pmid41621106,
year = {2026},
author = {Alhourani, A and Pouratian, N},
title = {Editorial. Defining value and function in miniaturized cortical arrays for human brain-computer interface applications.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E4},
doi = {10.3171/2025.11.FOCUS251041},
pmid = {41621106},
issn = {1092-0684},
}

@article {pmid41621105,
year = {2026},
author = {Vattipally, VN and Kramer, P and Troumouchi, K and Shiino, S and Abouelseoud, N and Joshi, K and Xu, R and Theodore, N and Brem, H and Bettegowda, C and Jantzie, LL and Robinson, S and Azad, TD and Kathuria, A},
title = {Engineered neuroglial organoids as living neural interfaces for restorative neurosurgery.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E5},
doi = {10.3171/2025.11.FOCUS25911},
pmid = {41621105},
issn = {1092-0684},
mesh = {*Organoids/transplantation/physiology ; Humans ; *Neuroglia/physiology/transplantation ; Animals ; *Brain-Computer Interfaces ; *Tissue Engineering/methods ; *Neurosurgical Procedures/methods ; Pluripotent Stem Cells ; Neurons/physiology ; },
abstract = {Acute and chronic CNS pathologies that result in tissue loss remain among the most intractable problems in neurosurgery, with current treatments focused on stabilization and neuroprotection rather than structural repair. Neural interfaces such as recording, stimulating, or replacing neural activity have demonstrated value in restoring function via prostheses and brain-computer interfaces, yet these approaches are constrained by electrode design, bandwidth, and limited biological integration. Engineered neuroglial organoids offer a complementary, biologically based interface strategy. Derived from pluripotent stem cells, neuroglial organoids arrive as 3D constructs containing neurons and glia in intrinsic architecture, capable of vascularization, synaptic connectivity, and integration with host tissue. Building on dissociated stem cell suspensions, organoids act not only as reservoirs of cells but also as living neural interfaces, receiving inputs from host circuits and generating functional outputs. Preclinical studies have demonstrated that transplanted organoids can couple to host sensory pathways, respond to stimulation, and support recovery of motor and cognitive functions. Moreover, emerging work coupling organoid grafts to brain-computer interfaces highlights the potential for closed-loop biological electronic systems, in which engineered devices provide precise recording and stimulation while organoids contribute adaptive, active biological circuits. This combination allows real-time bidirectional communication, allowing the graft to be both monitored and adapted to structurally and functionally integrate into host tissue. In this review, the authors examine neuroglial organoid transplantation through the lens of neural interfacing. They outline lessons from non-CNS organoid transplantation, summarize neurotrauma studies where grafts engage host circuits, and highlight opportunities to integrate organoids with electrodes, stimulation paradigms, and computational models. They also discuss challenges, namely vascularization, immune tolerance, surgical delivery, and manufacturing standards, that parallel those in neural device translation. For neurosurgeons, the appeal of neuroglial organoids lies not only in tissue replacement but in establishing a new class of biological neural interfaces, extending the reach of restorative neurosurgery. By merging living constructs with engineered devices, organoid-based strategies may enable hybrid restorative systems that restore function after neurological injury and disease.},
}

*Organoids/transplantation/physiology
Humans
*Neuroglia/physiology/transplantation
Animals
*Brain-Computer Interfaces
*Tissue Engineering/methods
*Neurosurgical Procedures/methods
Pluripotent Stem Cells
Neurons/physiology

@article {pmid41621104,
year = {2026},
author = {Johnson, TR and Moralle, S and Luo, Z and Taylor, DM},
title = {Implanting microelectrode arrays in the bottom of the central sulcus targeting somatosensory area 3a for restoration of proprioception.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E8},
doi = {10.3171/2025.11.FOCUS25916},
pmid = {41621104},
issn = {1092-0684},
mesh = {Animals ; Macaca mulatta ; Microelectrodes ; *Electrodes, Implanted ; *Somatosensory Cortex/surgery/physiology/diagnostic imaging ; *Proprioception/physiology ; *Brain-Computer Interfaces ; Male ; Stereotaxic Techniques ; Magnetic Resonance Imaging ; },
abstract = {OBJECTIVE: The long-term goal of this work is to develop a sensorimotor brain-machine interface (BMI) in which intended movements are decoded from the motor cortex and proprioceptive feedback is delivered via intracortical microstimulation of Brodmann's area 3a. A vital step toward this goal is to demonstrate in rhesus macaques a novel surgical approach for the precise and safe implantation of custom-length microelectrode arrays into area 3a at the bottom of the central sulcus.

METHODS: Preoperative planning combined high-resolution 7-T MR and CT imaging to generate 3D models of the cortices of 2 subjects. These models were used to fabricate 3D-printed skull replicas and to define a stereotactic trajectory that provided the shortest perpendicular path to the base of the central sulcus, where Brodmann's area 3a resides. Custom variable-length microwire electrode arrays were designed to span this target region. The flexibility of the microwires precluded the standard impact-insertion approach used with stiffer electrodes. Therefore, a custom vacuum-powered microdrive holder that moved with the pulsating brain was developed to maintain electrode orientation and to allow slow, controlled insertion along the planned trajectory. After implantation, the craniotomy was closed, and a skull-mounted recording chamber was secured. Postoperative verification of array placement was performed using CT imaging and neural recordings.

RESULTS: In both animals, imaging revealed that the base of the central sulcus was positioned anterior to its dorsal opening, making a precentral implant trajectory the shortest and most direct path to the bottom of the central sulcus. The integrated imaging and 3D modeling approach enabled accurate stereotactic placement of custom microelectrode arrays using the novel vacuum-assisted microdrive, as confirmed by postoperative CT imaging. Both surgical procedures were completed without complication, and isolatable neuronal spikes were recorded from multiple channels in each subject. In both animals, neural activity was modulated by passive movements of the arm.

CONCLUSIONS: Intracortical microelectrode implants for BMI applications have traditionally been limited to short (1.5-mm) electrodes targeting cortical sites exposed on the brain surface. The surgical methodology described here enables safe and accurate implantation of custom-length arrays into deep sulcal targets such as Brodmann's area 3a. By expanding access to previously inaccessible cortical regions, this approach broadens the potential neural information available for future BMI applications.},
}

Animals
Macaca mulatta
Microelectrodes
*Electrodes, Implanted
*Somatosensory Cortex/surgery/physiology/diagnostic imaging
*Proprioception/physiology
*Brain-Computer Interfaces
Male
Stereotaxic Techniques
Magnetic Resonance Imaging

@article {pmid41621103,
year = {2026},
author = {Lehner, KR and Luo, S and Greene, B and Angrick, M and Candrea, D and Husari, KS and Barth, K and Dister, J and Anushiravani, R and Miller, JS and Ho, E and Rincon-Torroella, J and Rapoport, B and Comair, Y and Crone, NE},
title = {Initial experience with the precision neuroscience Layer 7 micro-electrocorticography interface for real-time intraoperative neural decoding.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E3},
doi = {10.3171/2025.11.FOCUS25908},
pmid = {41621103},
issn = {1092-0684},
mesh = {Humans ; Adult ; *Electrocorticography/methods/instrumentation ; *Brain-Computer Interfaces ; Male ; Female ; Young Adult ; *Brain Neoplasms/surgery ; *Intraoperative Neurophysiological Monitoring/methods ; Craniotomy/methods ; Speech/physiology ; Brain Mapping/methods ; *Motor Cortex/physiology/surgery ; },
abstract = {OBJECTIVE: The aim of this study was to evaluate the feasibility of using the Layer 7 Cortical Interface, a high-density micro-electrocorticography (μECoG) array, for intraoperative neural recordings and real-time brain-computer interface (BCI) applications, including speech decoding and cursor control.

METHODS: Four patients (age range 23-43 years) who underwent awake craniotomy for tumor resection near the eloquent cortex were enrolled. The Layer 7 µECoG device (1024 channels, approximately 1.5-cm2 coverage) was placed on the motor cortex following standard cortical mapping. Intraoperative tasks included a joystick-controlled center-out movement paradigm (n = 3) and an auditory-cued speech repetition task (n = 1). Neural data were recorded at 20 kHz, preprocessed, and used to train decoders intraoperatively. A transformer-based model was applied for real-time speech synthesis and a convolutional neural network was trained for speech classification, while a convolutional recurrent neural network was trained to classify 2D cursor direction.

RESULTS: All 4 patients tolerated the procedure without device-related adverse events. The mean electrode impedances across 6 arrays (6144 channels) ranged from 1.21 to 1.99 MΩ, with 954-990 channels per array retained for analysis. In the speech task, a 4-word classification model achieved 77.5% accuracy, and a real-time synthesis model was able to distinguish speech and silence during approximately 20 minutes of data recording in the operating room. In the motor task, a 4-direction classification model achieved 78%-84% accuracy. Recordings remained stable during tumor resection.

CONCLUSIONS: The Layer 7 Cortical Interface device enabled high-resolution nonpenetrating cortical recordings that supported real-time speech classification and cursor control within the limited timeframe of an intraoperative session. These findings highlight the potential clinical applications of high-density µECoG for functional mapping, diagnostic assessment, and future chronic BCI systems for patients with motor and communication impairments.},
}

Humans
Adult
*Electrocorticography/methods/instrumentation
*Brain-Computer Interfaces
Male
Female
Young Adult
*Brain Neoplasms/surgery
*Intraoperative Neurophysiological Monitoring/methods
Craniotomy/methods
Speech/physiology
Brain Mapping/methods
*Motor Cortex/physiology/surgery

@article {pmid41621102,
year = {2026},
author = {Mortezaei, A and Al-Saidi, N and Taghlabi, KM and Hussein, A and Hallak, H and Pouratian, N and Faraji, AH},
title = {Brain-computer interfaces in poststroke rehabilitation: a meta-analysis of randomized clinical trials.},
journal = {Neurosurgical focus},
volume = {60},
number = {2},
pages = {E7},
doi = {10.3171/2025.11.FOCUS25913},
pmid = {41621102},
issn = {1092-0684},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; *Randomized Controlled Trials as Topic/methods ; *Stroke/therapy/physiopathology ; Recovery of Function/physiology ; },
abstract = {OBJECTIVE: Stroke is a leading cause of long-term disability, with conventional rehabilitation often failing to achieve substantial motor recovery, particularly in patients with severe paresis or in chronic stages. Brain-computer interfaces (BCIs) offer a novel rehabilitation approach by translating neural signals into real-time external feedback. The authors performed a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy and safety of noninvasive BCIs for poststroke motor rehabilitation.

METHODS: A systematic literature review was performed based on the PRISMA guidelines using 3 databases. Eligible RCTs enrolled stroke patients receiving noninvasive BCI-assisted motor rehabilitation compared with conventional therapies. The primary outcome was the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) improvement. Secondary outcomes included the Action Research Arm Test (ARAT), Motor Activity Log (MAL), Modified Barthel Index (MBI), and Modified Ashworth Scale (MAS). Effect sizes were pooled using random-effects models and expressed as mean differences (MDs), standardized MDs (SMDs), or odds ratios, each with corresponding 95% confidence intervals (CIs).

RESULTS: Thirty-two RCTs comprising 1187 patients were included with no heterogeneity or significant imbalances in baseline characteristics across groups. A BCI was significantly superior in FMA-UE score improvement compared with controls (MD 3.85, 95% CI 2.84-4.86; p < 0.01), with benefits sustained at follow-up. Within-group analyses revealed greater improvement in the BCI arm from follow-up to baseline (MD 8.18, 95% CI 5.77-10.60; p < 0.01). A BCI was also associated with higher ARAT (MD 7.18, 95% CI 2.4-12.0; p < 0.01) and MAL (SMD 0.59, 95% CI 0.34-0.85; p < 0.01) scores, although between-group differences for these endpoints were not statistically significant. For the MBI, a subgroup analysis did not demonstrate significant differences, but a sensitivity analysis revealed a significant improvement in the BCI group (p = 0.042). There were no significant differences in the within- and between-group analyses of the MAS. A subgroup analysis suggested a synergistic benefit with the BCI combined with neuromuscular electrical stimulation. Adverse events were infrequent and generally mild; 2 withdrawals in the BCI group were reported due to seizure and electrode allergy. Notably, all heterogeneity was successfully resolved through sensitivity analyses, supporting the robustness of the findings.

CONCLUSIONS: Noninvasive BCI-assisted rehabilitation is a safe and effective adjunct to conventional therapy, enhancing motor recovery after stroke. While all included RCTs evaluated noninvasive systems, the potential value and efficacy of invasive and minimally invasive BCIs may require further consideration.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation/methods
*Randomized Controlled Trials as Topic/methods
*Stroke/therapy/physiopathology
Recovery of Function/physiology

@article {pmid41620439,
year = {2026},
author = {Gong, Q and Fu, X and Feng, D and Rao, S and Pütz, B and Müller-Myhsok, B and Wei, L and Shen, C and Zhang, Y and Xu, L and Chen, W and Yang, K and Chen, D and Lv, X and Yan, Z and Luo, D and Wei, P and Jiang, H and Chen, W},
title = {Randomized, double-blind, sham-controlled pilot trial of theta-band transcranial alternating current stimulation during cognitive training in mild Alzheimer's disease.},
journal = {Translational psychiatry},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41398-026-03822-z},
pmid = {41620439},
issn = {2158-3188},
support = {82071181//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101581//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82371453//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Cognitive deficits are a hallmark of Alzheimer's disease (AD), and effective treatments remain elusive. Transcranial alternating current stimulation (tACS), a non-invasive technique, has shown potential in improving cognitive function across various populations, but further research is needed to investigate its efficacy in AD. In a randomized, double-blind, sham-controlled pilot trial, 36 mild AD patients received active or sham theta-tACS (8 Hz, 1.6 mA, 20-min daily) during n-back task for two weeks, followed by a 10-week follow-up. Cognitive assessments and resting-state EEG were analyzed at baseline, after-treatment, and follow-up. The results showed that the active group demonstrated significant cognitive improvements after treatment (MMSE: t (15) =-3.273, p = 0.005, Cohen's d = 0.82), particularly in short-term memory (MMSE-recall: Z = -2.11, p = 0.035, r = 0.53), with maintained benefits after 10 weeks. In contrast, the sham group exhibited long-term cognitive decline (MMSE: t (4) = 3.586, p = 0.023, Cohen's d = -1.60). EEG analysis revealed reduced gamma power (t (23) = 2.689, p = 0.013, Cohen's d = 1.077) and theta connectivity in active group, particularly in the frontotemporal regions (F4/F7: t (23) = 2.467, p = 0.021, Cohen's d = 0.988; F4/T3: t (23) = 2.465, p = 0.022, Cohen's d = 0.987), which was correlated with cognitive improvements (R = -0.57, p = 0.043). In conclusion, tACS combining cognitive training may offer cognitive benefits in mild AD by modulating neural activity, though further studies are needed to clarify its mechanisms.},
}

@article {pmid41620194,
year = {2026},
author = {Graham, F and Hutchinson, DW and Moon, TJ and Wang, J and Flores-Jimenez, H and Druschel, L and Ogunnaike, L and Gao, Y and Smith, T and DeTillio, S and Goelz, C and Bhalotia, A and Newman, L and Hess-Dunning, A and Capadona, JR and Karathanasis, E},
title = {Lipid Nanoparticle-Mediated Cd14 siRNA Delivery Ameliorates the Acute Inflammatory Response to Intracortical Microelectrode Implantation.},
journal = {Acta biomaterialia},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.actbio.2026.01.055},
pmid = {41620194},
issn = {1878-7568},
abstract = {Intracortical microelectrodes (IMEs) are an integral component of brain computer interfaces (BCIs) designed to study and treat neurological disorders. Unfortunately, IMEs tend to fail prematurely due in part to the macrophage-mediated inflammation in response to implantation injury and the persistent foreign body reaction. Previous work has established that cluster of differentiation 14 (CD14) is implicated in the neuroinflammatory response to IME implants. CD14 is a conserved damage-associated coreceptor that facilitates immune activation in the presence of inflammatory damage-associated stimuli. We sought to mitigate the inflammatory response to IME implantation by suppressing CD14 expression on macrophages using a lipid nanoparticle (LNP) loaded with Cd14-specific siRNA. We tested the efficacy of the LNP-mediated gene delivery in cultured murine macrophages and in an in vivo mouse model with IME implants. Our in vitro findings indicated that the LNPs suppress inflammatory cytokine secretion. The in vivo studies showed efficient targeting of the LNPs to the desired cell populations with the majority of LNPs found in blood-circulating macrophages and infiltrating macrophages at the intracortical implant site. Our results show that the LNPs efficiently silence expression of the targeted Cd14 gene. Suppression of the CD14 protein led to reduced infiltration of immune cells to the brain parenchyma, as well as a significant decrease of the inflammatory response to implantation within the first 24 hours after implantation, as determined by flow cytometry and transcriptomics. Together our results suggest that LNP-mediated gene therapy can specifically regulate one of the dominant drivers of the innate immune response to IME implantation. STATEMENT OF SIGNIFICANCE: Brain-computer interfaces rely on implanted electrodes to record and stimulate neural activity, but these devices often fail early because the body mounts an inflammatory immune response against them. Here, we focused on a central immune receptor, CD14, as a key driver of the inflammatory response to implants. Using lipid nanoparticles to deliver gene-silencing RNA, we were able to suppress CD14 expression in macrophages both in culture and in a mouse model with implanted electrodes. This targeted approach reduced immune cell infiltration and inflammation around implants. Our findings demonstrate that lipid nanoparticle-mediated gene therapy can selectively weaken the brain's innate immune response to implants, offering a promising strategy to improve the longevity and performance of neural interfaces.},
}

@article {pmid41619890,
year = {2026},
author = {Zhou, W and Chen, Y and Cen, K and Li, Z and Huang, C and Su, W and Li, P},
title = {Calcium carboxymethyl cellulose/quaternary ammonium chitosan self-gelling powder with good biocompatibility for wound hemostasis.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {150610},
doi = {10.1016/j.ijbiomac.2026.150610},
pmid = {41619890},
issn = {1879-0003},
abstract = {In this study, a multifunctional self-gelling hemostatic powder (CQA) was designed using natural biomaterials by integrating the antioxidant and biocompatible properties of Aloe vera gel (AV) with the hemostatic efficacy of calcium carboxymethylcellulose (Ca-CMC) and the antibacterial activity of quaternary ammonium chitosan (QCS). The CQA powder rapidly absorbs moisture upon contact with blood, forming a physically sealing hydrogel network through electrostatic and hydrogen bonding interactions. In vitro evaluations revealed that the optimized formulation, CQA0.3, exhibits outstanding adsorption capacity, antioxidant activity, and biocompatibility. Compared to commercial chitosan-based hemostatic powder (CS), CQA0.3 demonstrated significantly enhanced procoagulant performance, with a blood clotting index (BCI) of 8.48% versus 56.65% for CS, and promoted accelerated blood cell adhesion. In whole-blood coagulation assays, the CQA0.3 group achieved rapid clotting within 180 s, while bleeding persisted in the CS group beyond 210 s. In practical hemorrhage models, CQA0.3 reduced blood loss to 94.0 ± 8.7 mg, substantially lower than both the CQ group (225.7 ± 6.03 mg) and the CS group (292.7 ± 14.46 mg). These findings highlight the potential of CQA0.3 as a safe, efficient, and adaptable hemostatic agent for emergency and clinical applications, combining rapid gelation, high biocompatibility, and excellent wound adaptability.},
}

@article {pmid41618548,
year = {2026},
author = {Ma, Y and Li, H and Li, W and Jiang, D and Li, H and Xuan, X and Li, M},
title = {Noninvasive Graphene Brain-Computer Interface Integrating EEG Recording and Acoustic-Optical Stimulation for Rhythm Intervention.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e05327},
doi = {10.1002/adhm.202505327},
pmid = {41618548},
issn = {2192-2659},
support = {52271341//National Nature Science Foundation of China/ ; 62271350//National Nature Science Foundation of China/ ; },
abstract = {Noninvasive wearable stimulation-acquisition integrated brain-computer interfaces (BCIs) have significant application value in neurological rehabilitation and health monitoring. However, their widespread adoption depends on the development of long-term, stable dry/semi-dry electrodes and lightweight hardware. In this study, a sodium-doped vertical graphene (Na-VG) electrode that utilized sweat and tissue fluids as electrolytes was developed. When applied with ultrapure water, an extremely low electrode-skin impedance of 4.22 ± 0.50 kΩ was detected at 10 Hz. The 20-channel EEG cap assembled with the Na-VG electrodes maintained a high α-rhythm response of 5.06-14.22 dB in the signal-to-noise ratio of whole-brain EEG signals during a 36-day stability evaluation. Furthermore, a wearable Na-VG headband BCI combining sound-light stimulation and EEG acquisition was developed. Healthy individuals wearing this system, under the coordinated intervention of 40 Hz differential-frequency sound stimulation and 10 Hz light stimulation, showed changes in the frequency and amplitude of the α-rhythm. This improvement increased the proportion of moderate-levels of the vigilance index, neural activity, heart rate, emotion, and arousal index to 84-100%, with a precision of 98.73%. These results provide novel long-term, lightweight strategies and matching software and hardware for the monitoring and noninvasive intervention of emotional and cognitive-related diseases.},
}

@article {pmid41618424,
year = {2026},
author = {Campion, S and Navarro-Suné, X and Rivals, I and Morélot-Panzini, C and Serresse, L and Chavez, M and Demoule, A and Niérat, MC and Raux, M and Similowski, T},
title = {SSVEP-based brain-computer interface enabling graded dyspnoea self-report: proof-of-concept study in healthy volunteers.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12984-025-01846-y},
pmid = {41618424},
issn = {1743-0003},
abstract = {BACKGROUND: Mechanically ventilated patients may experience respiratory suffering, which is difficult to assess when verbal communication is impaired. We evaluated the performance of a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) designed to enable self-reporting of dyspnoea in this context.

METHODS: Forty-nine healthy volunteers were studied under five respiratory conditions: normal breathing (NB), inspiratory resistive loading (IRL), inspiratory threshold loading (ITL), CO₂ inhalation (CO₂), and a return to NB as wash-out (NBWO). Respiratory discomfort was evaluated using a visual analogue scale (VAS). Two BCIs models were tested: a detection BCI (D-BCI), designed to discriminate between 'breathing is OK' and 'breathing is difficult', and a quantification BCI in the form of a LED-based analogue scale (LAS), composed of five light-emitting diodes. Visual stimuli were delivered at different frequency sets: 12-15 Hz, 15-20 Hz, and 20-30 Hz for the D-BCI; low frequencies (13-17-19-23-29 Hz) and high frequencies (41-43-47-53-59 Hz) for the LAS. Performance was assessed using receiver operating characteristic (ROC) curves; the area under the ROC curve (AUC) was the primary outcome.

RESULTS: Participants reported significant respiratory discomfort during IRL, ITL, and CO₂ conditions in the D-BCI groups, and during ITL and CO₂ in the LAS groups, as reflected by higher dyspnoea VAS scores compared to NB. The best-performing frequency sets were 20-30 Hz for the D-BCI (AUC 0.89 [0.89-0.90]) and low frequencies for the LAS (AUC 0.84 [0.83-0.85]).

CONCLUSIONS: This study demonstrates that an SSVEP-based BCI can sucessfully detect and quantify experimentally induced dyspnoea in healthy individuals. Further research is needed to evaluate its clinical applicability for assessing dyspnoea in non-communicative patients.},
}

@article {pmid41617748,
year = {2026},
author = {Samuel, J and Murugan, TK and Govindaraj, L and Balaji, M and SenthilKumar, V and Sundararajan, S},
title = {Adversarial robust EEG-based brain-computer interfaces using a hierarchical convolutional neural network.},
journal = {Scientific reports},
volume = {},
number = {},
pages = {},
doi = {10.1038/s41598-025-34024-0},
pmid = {41617748},
issn = {2045-2322},
abstract = {Brain-Computer Interfaces (BCIs) based on electroencephalography (EEG) are widely used in motor rehabilitation, assistive communication, and neurofeedback due to their non-invasive nature and ability to decode movement-related neural activity. Recent advances in deep learning, particularly convolutional neural networks, have improved the accuracy of motor imagery (MI) and motor execution (ME) classification. However, EEG-based BCIs remain vulnerable to adversarial attacks, in which small, imperceptible perturbations can alter classifier predictions, posing risks in safety-critical applications such as rehabilitation therapy and assistive device control. To address this issue, this study proposes a three-level Hierarchical Convolutional Neural Network (HCNN) designed to improve both classification performance and adversarial robustness. The framework decodes motor intention through a structured hierarchy: Level 1 distinguishes MI from ME, Level 2 differentiates unilateral and bilateral motor tasks, and Level 3 performs fine-grained movement classification. The model is evaluated on the publicly available BCI Competition IV-2a dataset, which contains multi-class MI EEG recordings from nine healthy subjects. Robustness is assessed under gradient-based adversarial attacks, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool, across varying perturbation strengths, with adversarial training incorporated during learning. Experimental results show that the proposed HCNN achieves a clean-data accuracy of 91.2% and exhibits reduced performance degradation under adversarial attacks compared with conventional CNN baselines. These results indicate that hierarchical architectures offer a viable approach for improving the reliability of EEG-based BCIs. All experiments were conducted exclusively on the BCI Competition IV-2a dataset using EEG data from healthy subjects.},
}

@article {pmid41616114,
year = {2026},
author = {Yang, J and Huo, J and Liu, M and Feng, C and Zhang, Y and Pan, G and Meng, W and Han, R},
title = {vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e11922},
doi = {10.1002/advs.202511922},
pmid = {41616114},
issn = {2198-3844},
support = {2021YF F0704300//National Key Research and Development Program of China/ ; 62206158//National Natural Science Foundation of China/ ; 32371248//National Natural Science Foundation of China/ ; 2024CANAD-MES-061//Dubai Future Foundation/ ; //Fundamental Research Funds for the Central Universities/ ; 2025A1515010342//Natural Science Foundation of Guangdong Province/ ; ts20230204//Instrument Improvement Funds of Shandong University Public Technology Platform/ ; ZR2023YQ057//Natural Science Foundation of Shandong Province/ ; ZR2022QF097//Natural Science Foundation of Shandong Province/ ; //Shandong University Young Scholar Future Plan to W.M./ ; },
abstract = {Volume electron microscopy (vEM) is a powerful technique that enables 3D visualization of biological structures at the nanometer scale. However, vEM imaging relies on sequential scanning of 2D images, and due to section thickness limitations, the axial resolution is significantly lower than the lateral resolution. In this paper, we propose the vEMINR, an ultra-fast isotropic reconstruction method based on implicit neural representation (INR). This method enhances the reconstruction quality of vEM images by learning the true degradation patterns of low-resolution images, and significantly accelerates the reconstruction process by utilizing the efficient parameterization and a continuous function representation of INR. In experiments on 11 public datasets, vEMINR outperforms mainstream methods with over tenfold faster reconstruction and higher accuracy. vEMINR substantially improved the accuracy of organelle and neuron reconstruction from vEM. Overall, the excellent reconstruction time efficiency of vEMINR enables high-throughput processing of terabyte-scale vEM datasets while maintaining reconstruction accuracy. We believe that it will play a significant role in large-scale vEM image reconstruction and related research fields.},
}

@article {pmid41615974,
year = {2026},
author = {Ding, W and Liu, A and Wu, L and Cui, H and Fang, B and Chen, X},
title = {Data Augmentation for Subject-Independent SSVEP-BCIs via Simultaneous Spatial-Energy Representation.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3659606},
pmid = {41615974},
issn = {1558-2531},
abstract = {OBJECTIVE: Data augmentation is important for enhancing subject-independent classification in deep learning (DL) approaches for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) using electroencephalography (EEG). However, current augmentation techniques often inadequately exploit individual-specific style characteristics, limiting the model's robustness against inter-subject style variability. To tackle this problem, this study proposes a novel data augmentation method called Simultaneous Spatial-Energy Representation (SSER).

METHODS: SSER employs singular value decomposition (SVD) to extract spatial and energy representations from EEG signals, effectively capturing style characteristics. These representations are independently mixed across source domains during signal reconstruction, generating novel domains that cover a broader range of styles. This strategy promotes the learning of domain-invariant features and enhances the model's robustness to style variability.

RESULTS: Comprehensive experiments on public datasets demonstrate that SSER outperforms state-of-the-art data augmentation techniques and generalizes well across various DL models. Furthermore, self-collected offline and online experiments involving 30 subjects provide additional evidence of the method's effectiveness.

CONCLUSION: By simultaneously manipulating spatial and energy representations, SSER offers a richer characterization of EEG signal style variability, leading to superior performance.

SIGNIFICANCE: The proposed innovative data augmentation method advances subject-independent classification, facilitating the broader application of EEG-based BCIs in real-world scenarios.},
}

@article {pmid41554845,
year = {2026},
author = {Carević, I and Bajto, JŠ and Grubor, M and Štirmer, N},
title = {Wood biomass ash as a clinker substitute in advancing next-generation blended cement: Croatian case study.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {3932},
pmid = {41554845},
issn = {2045-2322},
support = {Project No. 101058162//HORIZON-CL4-2021-TWIN-TRANSITION-01/ ; },
abstract = {This research investigates the use of wood biomass ash (WBA) as a supplementary cementitious material (SCM) in blended cement formulations containing 6 and 12 wt% of bottom WBA. Motivated by the need to advance low-carbon cement production, reduce reliance on imported materials, and incorporate waste management strategies, the study explores sustainable pathways for cement manufacturing. Experimental results show that the 6 wt% WBA blend (BLEND BC-II) achieves a compressive strength of 59.3 MPa after 28 days, surpassing the reference CEM II, whereas the 12 wt% WBA blend (BLEND BC-I) also delivers favourable mechanical and durability performance, including a chloride diffusion coefficient of 15.85 × 10[-12] m[2]/s, capillary absorption of 0.68 g/m[2]·h[1]/[2], and gas permeability of 0.50 × 10[-16] m[2]. Volume stability tests of the 12 wt% WBA blend confirm that autogenous deformations remain below − 0.017 mm/m after 90 days, indicating effective mitigation of shrinkage and reliable dimensional stability. When combined with other SCMs, WBA further improves long-term mechanical performance. Despite challenges related to compositional variability and infrastructure requirements, WBA incorporation can reduce environmental impact and support low-carbon cement production. Achieving net-zero emissions extends beyond quantitative targets, requiring the restoration of balance between resource use, material efficiency, and environmental sustainability. These findings demonstrate that WBA is a viable SCM, advancing sustainable and resilient cement manufacturing.},
}

@article {pmid41613455,
year = {2025},
author = {Sun, Y and Wang, S and Gong, Y},
title = {Terahertz's silent revolution in physics, engineering, and life science: Beyond the spectrum.},
journal = {Fundamental research},
volume = {5},
number = {5},
pages = {1930-1932},
pmid = {41613455},
issn = {2667-3258},
abstract = {Terahertz technology is revolutionizing photonics, biomedicine, and communications by merging non-ionizing radiation with molecular sensitivity and material penetration. Advances in metamaterials, adaptive antennas, and AI-driven systems address historical limitations in emission efficiency and atmospheric attenuation, enabling secure high-capacity networks and precision biomedical applications. Reconfigurable beamforming and hybrid channel models enhance wireless reliability, while ultra-sensitive biosensors and neuromodulation techniques pioneer non-invasive diagnostics and therapies for neurodegenerative and psychiatric disorders. Terahertz's dual role in molecular sensing and neural modulation establishes closed-loop "detect-treat" paradigms, bridging material science and neuroscience. Challenges remain in optimizing clinical application and hybrid system scalability, yet its capacity to probe carrier dynamics, protein interactions, and neural circuits positions Terahertz as a universal platform for 6G networks, personalized medicine, and brain-machine interfaces. By unifying physics-aware engineering with biological insights, terahertz technology transcends traditional boundaries, offering transformative solutions for healthcare, secure connectivity, and industrial innovation.},
}

@article {pmid41613156,
year = {2025},
author = {Mohammadpour, H and Power, SD},
title = {Investigating singing imagery as an additional or alternative control task for EEG-based Brain-Computer Interfaces.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1736711},
pmid = {41613156},
issn = {1662-5161},
abstract = {INTRODUCTION: Brain-computer interfaces (BCIs) provide a movement-free means of communication and control, typically based on motor imagery (MI) tasks of hand, foot, or tongue movements. Most BCI studies focus on classifying up to four such tasks, which limits the number of available commands and restricts overall system functionality. Expanding the range of reliable mental tasks would directly increase the number of possible commands and thereby enhance the practical utility of BCIs. Singing imagery (SI) may offer an intuitive alternative or additional task to complement conventional MI paradigms.

METHODS: EEG data were recorded from 14 participants performing right-hand, left-hand, foot, and tongue MI, SI, and rest. Features were extracted using filter bank common spatial patterns (FBCSP), and tasks were classified with a random forest algorithm across 2-, 4-, 5-, and 6-class scenarios. Subjective data regarding participants' perceived task difficulty and general task preferences was also collected.

RESULTS: Classification accuracies with SI included were comparable to subsets of conventional MI tasks in 2-, 4-, and 5-class scenarios. In the 6-class scenario, average accuracy was approximately 60%, with six participants exceeding 70%, the level often cited as being necessary for effective BCI control. It is reasonable to expect performance to improve further with more advanced analysis methods and participant training.

CONCLUSION: These promising results suggest that singing imagery can serve as both an additional and an alternative task in MI-BCIs. In lower-class systems, SI may provide a valuable option for generating commands, particularly for users who may find some conventional MI tasks less intuitive. When combined with the established MI tasks, SI could increase the number of possible commands, thereby extending the functional capacity of BCI systems. Overall, this work demonstrates the potential of SI to broaden the repertoire of mental tasks available for BCI control and to advance the development of more flexible, powerful, and user-centered BCI applications.},
}

@article {pmid41612341,
year = {2026},
author = {Powell, J and Zhou, A},
title = {Brain-computer interface commercialization.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {45},
pmid = {41612341},
issn = {1743-0003},
}

@article {pmid41611752,
year = {2026},
author = {Aars, J and Ieno, EN and Andersen, M and Derocher, AE and Wiig, Ø and Zuur, AF},
title = {Body condition among Svalbard Polar bears Ursus maritimus during a period of rapid loss of sea ice.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {2182},
pmid = {41611752},
issn = {2045-2322},
mesh = {Animals ; *Ursidae/physiology ; *Ice Cover ; Female ; Male ; Arctic Regions ; Svalbard ; Ecosystem ; Climate Change ; },
abstract = {Polar bears are only found in Arctic areas with sufficient access to sea ice and seals on which they prey. Studies have highlighted negative effects on condition and demographics in areas where sea ice cover is declining due to warmer climate, but condition of the Barents Sea polar bear population have not been examined yet. Loss of sea ice rate has been considerably higher here than in other areas with polar bears. We investigated variation in body condition index (BCI) among 770 adult bears, 1188 captures, in March-May 1995-2019, in Svalbard, Norway (western part of the Barents Sea). We assessed how intrinsic (female reproductive state, age) and both males and females, BCI declined until 2000, but increased afterwards, during a period with rapid loss of sea ice. In models including sea ice metrics and climate (Arctic Oscillation), there was no support for the predicted negative effect of warmer weather and habitat loss. This indicates a complex relationship between habitat, ecosystem structure, energy intake, and energy expenditure. Increases in some prey species, including harbour seals, reindeer, and walrus, may partly offset reduced access to seals. Our findings underline the importance not to extrapolate findings across populations.},
}

Animals
*Ursidae/physiology
*Ice Cover
Female
Male
Arctic Regions
Svalbard
Ecosystem
Climate Change

@article {pmid41611285,
year = {2026},
author = {Zan, T and Gao, YS},
title = {[Reconstruction of superficial organs: a leap from structural restoration to functional rehabilitation].},
journal = {Zhonghua shao shang yu chuang mian xiu fu za zhi},
volume = {42},
number = {1},
pages = {26-33},
pmid = {41611285},
issn = {2097-1109},
support = {T2024076//National High-Level Talents Program/ ; 82272264, 82472557//General Program of National Natural Science Foundation of China/ ; 2023ZZ02023//Shanghai Plastic Surgery Research Center of Shanghai Priority Research Center/ ; },
mesh = {Humans ; *Tissue Engineering/methods ; *Regenerative Medicine/methods ; *Plastic Surgery Procedures/methods ; Printing, Three-Dimensional ; Artificial Intelligence ; Tissue Scaffolds ; },
abstract = {The core objective of superficial organ reconstruction is to perfectly restore the organ's morphological structure and biological function. Currently, significant progress has been achieved in structural construction, blood supply assurance, and morphological and functional reconstruction of superficial organ reconstruction, primarily relying on approaches including surgical techniques, tissue engineering, and regenerative medicine. In the future, with the integration and application of cutting-edge technologies such as gene editing, artificial intelligence, three-dimensional printing, and brain-computer interfaces, superficial organ reconstruction is poised to enter a new historical stage characterized by high intelligence, precision, and comprehensive functional restoration. This article focuses on superficial organ reconstruction, systematically outlines its concept, challenges, and current development status, and proposes future perspectives for this field.},
}

Humans
*Tissue Engineering/methods
*Regenerative Medicine/methods
*Plastic Surgery Procedures/methods
Printing, Three-Dimensional
Artificial Intelligence
Tissue Scaffolds

@article {pmid41525560,
year = {2026},
author = {Siviero, I and Vale, N and Menegaz, G and Ramos-Murguialday, A and Francesca Storti, S},
title = {Artificial Intelligence and Wearable Technologies for Upper Limb Neurorehabilitation.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {34},
number = {},
pages = {732-749},
doi = {10.1109/TNSRE.2026.3651949},
pmid = {41525560},
issn = {1558-0210},
mesh = {Humans ; *Wearable Electronic Devices ; *Artificial Intelligence ; *Neurological Rehabilitation/methods/instrumentation ; *Upper Extremity/physiopathology ; Electromyography ; Electroencephalography ; Brain-Computer Interfaces ; },
abstract = {Non-invasive neural interfaces (NIs) are increasingly investigated in upper limb neurorehabilitation, where they exploit biosignals, such as electroencephalography (EEG) and electromyography (EMG), to decode motor intentions using artificial intelligence (AI). Yet, traditional systems are complex and difficult to use outside the clinic. Wearable devices have the potential for innovative neurorehabilitation solutions thanks to their comfort, easy-to-use and long-term monitoring. However, current AI approaches require adaptation to the technical constraints of wearable devices, and the related state-of-the-art is not clearly explained and summarized. In this work, a systematic literature review on 51 studies was conducted analyzing them according to five important concepts: biosignals, wearable devices, AI-driven methods, upper limb, and clinical applications. The review highlights methodological heterogeneity, a variety of wearable sensor configurations, and open challenges related to accuracy, robustness, and clinical validation. Finally, we discuss how explainable AI (XAI) and generative AI (GenAI) may contribute to improve the interpretability and personalization of future neurorehabilitation systems.},
}

Humans
*Wearable Electronic Devices
*Artificial Intelligence
*Neurological Rehabilitation/methods/instrumentation
*Upper Extremity/physiopathology
Electromyography
Electroencephalography
Brain-Computer Interfaces

@article {pmid41462297,
year = {2025},
author = {Zhao, Z and Duan, X and Luo, J and He, Z and Zhang, Y and Wang, M and Qin, J and Lin, S and Chen, H},
title = {Spatiotemporal dynamics of neuronal subtypes and their interactions with glia following intracortical electrode implantation.},
journal = {Biology direct},
volume = {21},
number = {1},
pages = {13},
pmid = {41462297},
issn = {1745-6150},
support = {32201095//National Natural Science Foundation of China/ ; },
abstract = {BACKGROUND: Chronically implanted electrodes offer a promising approach for treating neurological disorders via brain-computer interfaces, yet their long-term efficacy is compromised by the neuroinflammatory foreign body response. While neurons are central to both electrode function and inflammatory regulation, their specific responses post-implantation remain poorly characterized. Here, we combined single-nucleus RNA sequencing (snRNA-seq) and immunofluorescence to delineate the spatiotemporal dynamics of neuronal subtypes in the rat motor cortex at 3, 25, and 50 days after electrode implantation.

RESULTS: We identified 22 distinct neuronal subpopulations, among which clusters 5, 6, and 8 emerged as injury-responsive subtypes during the acute phase (3 days), exhibiting a specific upregulation of Tmsb4x, a key regulator of neuronal plasticity and repair. Furthermore, our analysis revealed activated signaling pathways mediating neuron-glia communication, most notably the Ptn-Sdc4 and Il34/Csf1-Csf1R axes between neurons and astrocytes.

CONCLUSIONS: These findings provide a high-resolution map of neuronal adaptation to intracortical implants, uncovering previously unknown repair-associated neuronal subtypes and specific ligand-receptor pairs that coordinate the neuroinflammatory microenvironment, which offers novel insights and potential therapeutic targets for improving the biocompatibility and long-term stability of neural electrodes.

GRAPHICAL ABSTRACT: [Image: see text]

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13062-025-00719-7.},
}

@article {pmid41610453,
year = {2026},
author = {Eguinoa, R and San Martín, R and Luna, P and Herrojo-Ruiz, M and Vidaurre, C},
title = {An EEG correlation framework to study state anxiety and learning under uncertainty.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3f58},
pmid = {41610453},
issn = {1741-2552},
abstract = {Objective.Recent developments in computational neuroscience have shed light on the neural processes underlying altered decision-making under uncertainty in anxiety. These disruptions are partly attributed to impaired encoding of precision-weighted prediction errors (pwPEs), which guide belief updating during learning and decision-making, as described by hierarchical Bayesian models. In this paper, we introduce a gamified paradigm for collecting decision-making data, together with a framework for extracting EEG features linked to computationally relevant variables, drawing on principles from neurofeedback and brain-computer interface research. This approach aims to develop tools that target functionally meaningful brain networks involved in decision-making, with the potential to inform future neurofeedback interactions.Approach.Forty healthy participants performed a volatile decision-making task in a game-based, immersive environment. EEG data were analysed to identify spatial filters whose theta- and alpha-band power correlated with pwPEs and state anxiety scores. Both intra-subject (trial-wise pwPEs) and intersubject (state anxiety) analyses were conducted to uncover distinct neural signatures.Main results.The intra-subject analysis revealed that pwPEs were significantly and positively correlated with theta power, and significantly and negatively correlated with alpha power - supporting the hypothesis that these oscillatory patterns underlie belief updating. In contrast, the inter-subject analysis showed that higher state anxiety was associated with reduced theta and increased alpha power, consistent with attenuated learning and impaired adaptation in anxious individuals. These findings align with theoretical models of hierarchical Bayesian inference and prior evidence of anxiety-related disruptions in uncertainty processing.Significance.The findings validate the proposed EEG framework for identifying neural markers related to belief updating and anxiety-related learning impairments. This approach lays the foundation for personalized neurofeedback procedures that target maladaptive decision-making in anxiety, with the added benefit of using immersive task paradigms for better engagement and translational potential for real-world applications.},
}

@article {pmid41609332,
year = {2026},
author = {Cicciarella, R and Willems, EP and Markham, B and Bizzozzero, MR and Phillips, W and Allen, SJ and Krützen, M and Christiansen, F},
title = {Validation of aerial photogrammetry methods to measure body size, condition and mass in small cetaceans.},
journal = {The Journal of physiology},
volume = {},
number = {},
pages = {},
doi = {10.1113/JP290419},
pmid = {41609332},
issn = {1469-7793},
support = {//A.H. Schultz Foundation/ ; 310030_204974/SNSF_/Swiss National Science Foundation/Switzerland ; },
abstract = {Accurate morphometric measurements are essential for estimating body size and condition in animals. These characteristics are, in turn, key to eco-physiological studies, wildlife management and conservation. For free-ranging cetaceans, however, collecting non-invasive morphometric data is challenging. Unoccupied aerial vehicle (UAV) photogrammetry offers a promising solution but requires ground-truthing to assess accuracy and precision. Similarly, morphometric-based indices of body condition must be validated against the animals' true body condition. Here we validated UAV-derived estimates of body size and condition in bottlenose dolphins (Tursiops spp.) under human care by comparing photogrammetry-based measurements of body length, width, height and girth from both stationary and swimming individuals with manual measurements. The two methods showed negligible differences, with UAV-based data yielding lower variability, confirming both high measurement accuracy and precision. Using UAV-derived measurements we calculated a volume-based body condition index (BCI) and compared it with a mass-based BCI, a standard metric in ecological research. The two indices showed a near-perfect fit, demonstrating that volume-based metrics reliably reflect true body condition in small cetaceans. Body density decreased with increasing body condition, consistent with higher fat-to-muscle ratios. By combining UAV-derived body volume with predicted density, based on their body condition, we accurately estimated individual body mass (mean error = 6.4%). This study provides a comprehensive validation of UAV-based photogrammetry to estimate body size, condition and mass in small cetaceans, highlighting its value as a non-invasive and cost-effective tool for ecological and conservation research. KEY POINTS: Measuring body size and condition in free-ranging dolphins is difficult, yet essential to understand their physiology, energy reserves and health. We used unoccupied aerial vehicles (UAV) to obtain accurate, non-invasive body measurements of bottlenose dolphins and compared them with direct manual measurements. UAV-based photogrammetry produced highly precise and accurate estimates of body length, girth and overall body volume, even for freely swimming animals. A UAV-derived, volume-based body condition index matched traditional mass-based indices and enabled accurate estimation of body mass. These results validate UAV photogrammetry as a reliable, ethical and cost-effective method for assessing body size, condition and mass in small cetaceans, thereby advancing ecological and physiological research in the wild.},
}

@article {pmid41606732,
year = {2026},
author = {Hu, L and Ye, L and Ye, H and Liu, X and Xiong, K and Zhang, Y and Zheng, Z and Jiang, H and Chen, C and Shen, C and Wang, Z and Zhou, J and Wu, Y and Huang, K and Zhu, J and Chen, Z and Ding, M and Weiss, S and Yang, D and Wang, S},
title = {Harmonic patterns embedded in ictal EEG signals in focal epilepsy: new insight into the epileptogenic zone.},
journal = {BMC medicine},
volume = {},
number = {},
pages = {},
doi = {10.1186/s12916-026-04665-7},
pmid = {41606732},
issn = {1741-7015},
support = {82171437//The National Natural Science Foundation of China/ ; LD24H090003//The Natural Science Foundation of Zhejiang Province/ ; },
abstract = {BACKGROUND: Localization of the epileptogenic zone (EZ) requires further refinement. We identified a unique ictal spectral structure, the "harmonic pattern" (H pattern), which potentially serves as a novel biomarker for localizing the EZ. This study aimed to analyze the clinical significance of the H pattern and to explore its underlying waveform features.

METHODS: Seventy patients with drug-resistant focal epilepsy, undergoing stereo-EEG (SEEG) evaluation and surgery, were included. Time-frequency maps (TFM) were generated using Morlet wavelet transform analysis. The H pattern was defined as multiple equidistant, high-density bands with varying frequencies on TFM. The upper quartile was employed to confirm contacts expressing dominant H pattern (dH pattern). Bispectral analysis and transfer function modeling were employed to assess nonlinear properties and signal propagation, respectively. The performance of the dH pattern in evaluating the EZ was compared with other ictal biomarkers.

RESULTS: Regardless of seizure onset patterns, the H pattern commonly occurred during early or late seizure propagation among 57 patients (81.4%). It harbored within specific EEG segments characterized by fast activity and irregular polyspikes. The H pattern often appeared simultaneously across different brain regions at a consistent fundamental frequency, highlighting a crucial stage in seizure propagation characterized by inter-regional synchronization. The dH pattern demonstrated greater nonlinearity compared to the non-dH pattern, as evidenced by bispectral analysis. The waveforms associated with the dH pattern were more stereotyped and showed increased skewness and/or asymmetry. Notably, the complete removal of areas exhibiting the dH pattern, but not high epileptogenicity index (≥ 0.3) or seizure onset zone, was independently associated with seizure freedom after surgery.

CONCLUSIONS: The H pattern provides unique insights into ictal neural dynamics. Additionally, it is a novel and alternative approach for measuring the EZ over an extended ictal time window.},
}

@article {pmid41606342,
year = {2026},
author = {Xu, Z and Wang, H and Yu, J and Deng, Y and Tian, X and Ni, R and Xia, F and Yang, L and Xu, C and Zhang, L and Luo, R and Chen, P and Zhang, X and Liu, Y and Hou, J and Zhang, M and Chen, S and Su, L and Sun, H and He, Y and Chen, D and Chen, X and Miao, Z and Xie, J and Liu, X and Zhao, J and Ke, B and Tian, X and Zeng, L and Zhang, L and Tang, X and Yang, S and Liu, J and Wang, X and Yan, W and Shao, Z},
title = {Psychedelics elicit their effects by 5-HT2A receptor-mediated Gi signalling.},
journal = {Nature},
volume = {},
number = {},
pages = {},
pmid = {41606342},
issn = {1476-4687},
abstract = {Psychedelics are undergoing a renaissance as potential therapy for psychiatric disorders, with more than 200 clinical trials being studied across several countries[1-3]. However, the precise mechanisms by which these drugs bring about benefits and the potential clinical risks are not yet fully understood. The serotonin 2A receptor (5-HT2AR) was reported to be a Gq-coupled receptor and the primary interoceptive target of psychedelics[4,5]. Here we compared psychedelics and their non-hallucinogenic analogues (nHAs) using in vitro and in vivo approaches, finding that 5-HT2AR-mediated non-canonical Gi signalling is essential for hallucinogenic effect. We further presented five cryo-electron microscopy structures of 5-HT2AR-Gi/Gq in complex with psychedelics or nHAs. Structural analysis and pharmacological investigation revealed that a special contact between nHAs with 5-HT2AR mediated the signalling bias. Building on this insight, we identified a 2,5-dimethoxy-4-iodoamphetamine derivative, DOI-NBOMe, which exhibits potent and selective Gq-biased activity, and demonstrates promising therapeutic effects in mouse models without hallucinogenic effect. Our finding uncovers the functional mechanisms underlying the Gi signalling mediated by 5-HT2AR and provides valuable insights for designing psychedelic-based drugs with minimized risk from hallucinogenic effects.},
}

@article {pmid41605490,
year = {2026},
author = {Chen, H and Yun, G},
title = {Efficacy of Brain-Computer Interface Therapy for Upper Limb Rehabilitation in Chronic Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e79132},
doi = {10.2196/79132},
pmid = {41605490},
issn = {1438-8871},
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods ; Randomized Controlled Trials as Topic ; *Upper Extremity/physiopathology ; Activities of Daily Living ; Chronic Disease ; *Stroke/physiopathology ; },
abstract = {BACKGROUND: Over 50% of people with chronic stroke experience persistent upper limb dysfunction. Brain-computer interface (BCI) therapy, creating a sensorimotor loop via neural feedback, is a promising alternative; yet, its optimal application remains unclear.

OBJECTIVE: This meta-analysis evaluates BCI's efficacy on motor function, tone, and activities of daily living (ADL) in chronic stroke and identifies optimal feedback modalities and intervention parameters.

METHODS: We systematically searched Cochrane Library, Embase, PubMed, Scopus, Web of Science, and Wanfang Data from inception to October 2025 for randomized controlled trials (RCTs) comparing BCI-based training to control interventions in adults with chronic stroke. Primary outcomes were upper limb motor function (Fugl-Meyer Assessment for upper extremity [FMA-UE], Action Research Arm Test [ARAT]), muscle tone (Modified Ashworth Scale [MAS]), and ADL (Modified Barthel Index [MBI], Motor Activity Log [MAL]). Screening, data extraction, and risk-of-bias assessment were performed independently. Meta-analysis used a random-effects model with Hartung-Knapp-Sidik-Jonkman adjustment. Pooled mean differences (MDs) with 95% CIs and 95% prediction intervals (PIs) were calculated. Subgroup analyses examined feedback modalities, intervention intensity, and follow-up effects. Sensitivity analysis was also conducted.

RESULTS: From 3529 records, 21 RCTs (650 participants) were included. BCI training significantly improved motor function (FMA-UE: MD 2.50, 95% CI 0.60-4.40; P=.01; 95% PI -2.52 to 7.22) and ADL performance (MBI: MD 8.38, 95% CI 2.23-14.53; P=.02; 95% PI -3.92 to 20.53; MAL: MD 2.09, 95% CI 0.42-3.76; P=.03; 95% PI -0.69 to 4.54). No significant effects were observed for fine motor skills (ARAT: MD 0.18, 95% CI -0.27 to 0.62; P=.30; 95% PI -3.64 to 3.99) or muscle tone (MAS: MD -0.48, 95% CI -1 to 0.03; P=.06; 95% PI -1.27 to 0.35). Subgroup analyses revealed that BCI-functional electrical stimulation (FES) yielded the greatest improvement in motor recovery (FMA-UE: MD 5, 95% CI 1.86-8.13; P=.01). The optimal intervention protocol was identified as 30-minute sessions, administered 4-5 times per week over 2 weeks (total of 10-12 sessions). However, benefits were not sustained at follow-up.

CONCLUSIONS: Low- to moderate-certainty evidence suggests that BCI training, particularly the BCI-FES paradigm, can improve upper limb motor function and ADL in people with chronic stroke on average. However, wide prediction intervals indicate the effect may vary substantially across settings, ranging from negligible to beneficial. Subgroup analyses suggested a potential optimal protocol of 30-minute sessions, 4-5 times per week for 2 weeks, but these findings are limited by the small number of studies in each subgroup and the high risk of bias in several included trials. Therefore, this proposed protocol should be viewed as preliminary and requires validation in future, high-quality RCTs. Future research should also focus on identifying patient subgroups most likely to benefit and on strategies to sustain long-term gains.

TRIAL REGISTRATION: PROSPERO CRD420251063808; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251063808.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation/methods
Randomized Controlled Trials as Topic
*Upper Extremity/physiopathology
Activities of Daily Living
Chronic Disease
*Stroke/physiopathology

@article {pmid41605384,
year = {2026},
author = {Jiang, H and Fu, H and Wei, Q and Wang, Y},
title = {A hierarchical bilayer sponge dressing based on QCMCS@GO/PLA for synergistic wound healing via hemostasis and anti-adhesion.},
journal = {International journal of biological macromolecules},
volume = {},
number = {},
pages = {150565},
doi = {10.1016/j.ijbiomac.2026.150565},
pmid = {41605384},
issn = {1879-0003},
abstract = {To address the challenges of inefficient hemostasis, high risk of bacterial infection, and biofilm formation in wound management, this study developed a bilayered sponge dressing composed of quaternized carboxymethyl chitosan@ graphene oxide/polylactic acid (QCMCS@GO/PLLA) with triple functionalities: coagulation, antibacterial activity, and anti-adhesion. A hierarchical structure was constructed using freeze-drying and electrospinning techniques: the bottom layer is a QCMCS@GO composite sponge, where graphene oxide (GO) enhances mechanical strength and enriches coagulation factors, while the quaternized carboxymethyl chitosan (QCMCS) promotes platelet activation and intrinsic coagulation pathway via its cationic properties; the top layer consists of electrospun polylactic acid (PLLA) nanofibers that serve as a superhydrophobic physical barrier to effectively inhibit bacterial adhesion. The material exhibits high porosity (>92%) and rapid liquid absorption (≥95% within 40 ms). In vitro experiments demonstrated that the dressing significantly accelerated whole blood coagulation (time reduced by 52.3%), optimized the blood clotting index (BCI = 4.7%), and enhanced thrombus formation through FXII contact activation. It achieved bacterial eradication rates of 99.94% against Staphylococcus aureus and 99.61% against Escherichia coli, while reducing bacterial adhesion on the surface by 91.8%. The dressing showed excellent biocompatibility (hemolysis rate 2.3%, cell proliferation rate 138%). In a rat liver injury model, it shortened hemostatic time by 63.2% and reduced blood loss by 76.5% compared to commercial gelatin sponges. This study provides a novel strategy for developing multifunctional wound dressings.},
}

@article {pmid41605140,
year = {2026},
author = {Ravi, A and Jiang, N and Tung, J},
title = {EEG-Based Gait Phase Decoding from Combined Action Observation and Motor Imagery.},
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.2026.3659011},
pmid = {41605140},
issn = {1558-0210},
abstract = {Gait recovery is a crucial component of stroke rehabilitation. While Brain-Computer Interfaces (BCIs) decoding motor intent from motor imagery (MI) have shown success, their application in the area of gait phase decoding remains limited. Combining Action Observation (AO) and MI paradigms have demonstrated enhanced motor cortex activation compared to AO or MI alone. This study investigated the feasibility of decoding swing and stance phass of gait from electroencephalogaphy (EEG), via a proposed feature extraction and classification method. A novel dataset, utilizing the Combined AO, MI, and Steady-State Motion Visual Evoked Potential (SSMVEP) (CAMS-BCI) paradigm, was collected from twenty healthy volunteers. Employing an innovative labelling technique, three different classification methods were compared. Among them, broad band EEG features with a linear classifier achieved the highest average f1-score of 0.77 in gait phase classification. Additionally, the methods achieved an overall accuracy of 70% in classifying individual Swing and Stance phases based on the CAMS stimulus responses. These findings provide valuable insights for the development of novel BCI feedback mechanisms specifically targeting different phases of gait. Implementing them in future designs can potentially enhance gait recovery outcomes in post-stroke rehabilitation.},
}

@article {pmid41604716,
year = {2026},
author = {Lu, J and Liu, Y and Zhang, X and Han, J and Fan, Z and Yu, N},
title = {A principal brain-region analysis framework based on evolutionary decomposition for fNIRS brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae3eb7},
pmid = {41604716},
issn = {1741-2552},
abstract = {Functional near-infrared spectroscopy (fNIRS) is an emerging technique for brain-computer interfaces (BCIs) due to its advantages in spatial resolution, robustness to artifacts, portability and usability for long-term monitoring, etc. Existing BCI methods take a holistic approach to all signal-collecting channels and corresponding brain regions, while the task-related brain regions and their interactions have not been well explored. Approach. This paper proposes a principal brain-region analysis (PBA) framework to incorporate the functional contribution as well as collaboration of task-specific brain regions (TSBRs) to boost BCI performance. Firstly, the identification of TSBRs is formulated as an optimization problem by maximizing classification accuracy under spatial constraints on brain regions of interest. Then, an evolutionary decomposition algorithm is constructed by combining spatial nondominated operators and genetic iterative computation, identifying TSBRs from the whole brain regions. Afterwards, classifiers are trained by neuroimaging features in the decomposed TSBRs in combination with stacking to generate the final predictions. Results. The proposed PBA method was evaluated on two public datasets for fNIRSbased BCIs, significantly enhancing the classification accuracy for the sliding slopebased method by 8.91% and 6.03% and the sliding mean concentration change method by 13.62% and 6.15%, respectively. Significance. Principal brain-region analysis establishes a pivotal framework to fundamentally advance the accuracy and explainability of BCIs.},
}

@article {pmid41604633,
year = {2026},
author = {Bialostocki, LS and Adhia, DB and Mudiyanselage, DR and Smith, ML and Cakmak, YO and De Ridder, D and Mani, R and Mathew, J},
title = {Authors' Reply: Bridging Neurofeedback and Structural Connectivity in Chronic Pain.},
journal = {JMIR research protocols},
volume = {15},
number = {},
pages = {e89007},
doi = {10.2196/89007},
pmid = {41604633},
issn = {1929-0748},
}