@article {pmid42674229,
year = {2026},
author = {Wang, Z and Zang, Z and Li, Y and Huang, Z and Zhang, H and Fu, Q and Bai, R},
title = {Water-exchange pharmacokinetic modeling improves DCE-MRI for ROI-based assessment of prostate lesions and of clinically significant prostate cancer.},
journal = {Magnetic resonance imaging},
volume = {134},
number = {},
pages = {110778},
doi = {10.1016/j.mri.2026.110778},
pmid = {42674229},
issn = {1873-5894},
abstract = {PURPOSE: Quantitative multiparametric MRI (mpMRI) is increasingly explored for clinically significant prostate cancer (csPCa) detection; however, the diagnostic value of quantitative dynamic contrast-enhanced (DCE)-MRI in prostate cancer remains controversial. In this study, we aim to evaluate the diagnostic performance of water-exchange DCE-MRI in distinguishing prostate tumors from benign tissue and differentiating csPCa from clinically insignificant (CIS) disease.

METHODS: This retrospective study included 89 patients who underwent prostate DCE-MRI between March 2022 and October 2023. DCE-MRI quantitative analysis was based on two-site water exchange (2SX) and conventional Tofts model. Tumor, benign and benign prostatic hyperplasia regions of interest were drawn based on quantitative multiparametric MRI. Student's t-test was implemented for intergroup comparisons between benign tissue and tumor, and between CIS and csPCa. Diagnostic performance for differentiating tumor from benign tissue and csPCa from CIS was accessed via linear discriminant analysis with tenfold cross-validation.

RESULTS: Fifty-nine patients (mean age ± standard deviation, 69 ± 8 years) were finally included. Most MRI voxels in tumor (86%) and benign tissue (76%) favored 2SX model over Tofts model based on corrected Akaike's Information Criterion. The volume transfer constant (K[trans]) and cellular water efflux rate constant (kio) from 2SX model were significantly higher in tumor regions than benign regions (both P < 0.0001). Compared with the Tofts model, the 2SX model improved discrimination between tumor and benign tissue. Moreover, incorporating kio to correct K[trans], 2SX model significantly enhanced the ability of K[trans] to differentiate csPCa from CIS compared with Tofts model (area under curve: 0.72 vs 0.63).

CONCLUSION: The 2SX model incorporating transmembrane water exchange into quantitative DCE-MRI analysis improves diagnostic performance over the conventional Tofts model alone for distinguishing prostate tumor from benign tissue and for differentiating csPCa from CIS.},
}

@article {pmid42677815,
year = {2026},
author = {Aziz, MZ and Zhuo, Y and Huang, B and Yu, X},
title = {SEDAT: A hybrid tokenizer for large EEG models.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/aea0ba},
pmid = {42677815},
issn = {1741-2552},
abstract = {OBJECTIVE: The fidelity of neural representations learned by large EEG foundation models depends on how raw brain signals are tokenized. Existing methods suffer from arbitrary temporal boundaries misaligned with neural state transitions, neglecting inter-channel spatial information, and fixed segmentation criteria that fail to generalize across heterogeneous EEG paradigms.

APPROACH: This study proposes the SE-DAGAF Adaptive Tokenizer (SEDAT), a hybrid framework integrating squeeze and- excitation (SE)-based spatial aggregation, data-adaptive Gaussian average filtering (DAGAF)- based signal decomposition, instantaneous-frequency-guided adaptive segmentation, and Fourier domain resampling into a single computationally efficient pipeline. SEDAT is evaluated across 10 heterogeneous EEG datasets spanning motor imagery, mental imagery, P300, slow cortical potentials, sleep staging, and epilepsy paradigms, using four large foundation models: LaBraM, EEGFormer, EEGPT, and NeuroGPT. It is benchmarked against five competitive baselines: fixed length windowing, CTXSEG, LiPCoT, TFM-Tokenizer, and SiS.

MAIN RESULTS: SEDAT achieves classification improvements of up to 15.3% over fixed-length windowing and 1.2-4.6% over the second-best method, with all comparisons reaching p < 0.001 after Benjamini-Hochberg correction. Token quality analysis confirms substantially improved feature separability, with Silhouette scores of 0.81-0.85 versus 0.33-0.48 for rigid baselines.

SIGNIFICANCE: With O(CN + KN logN) complexity, SEDAT explores new applications for SE and DAGAF as tokenizers and provides a physiologically grounded and computationally practical tokenization solution for large-scale EEG foundation models.},
}

@article {pmid42678134,
year = {2026},
author = {Coraci, D and Regazzo, G and Masiero, S},
title = {Mapping the literature about brain-computer interface in rehabilitation: a graph-theory-based PCA framework for semantic space analysis.},
journal = {European journal of translational myology},
volume = {},
number = {},
pages = {},
doi = {10.4081/ejtm.2026.15674},
pmid = {42678134},
issn = {2037-7452},
abstract = {Brain-Computer Interfaces (BCIs) are increasingly used in neurorehabilitation, but the rapid expansion of scientific literature complicates the identification of clinically relevant studies. This study investigated whether expert-defined relevance within BCI rehabilitation literature emerges as a structural property of semantic networks through the integration of graph theory and Principal Component Analysis (PCA). A Lexical Network Analysis Based on Graph Theory (LENGTH) was applied to randomized controlled trials indexed in PubMed over the last decade using the query "brain computer interface" AND rehabilitation. Titles and abstracts were analyzed to construct a semantic network linking articles and lexical terms. Multiple graph-theoretical metrics were calculated and residualized against weighted degree to minimize document-size bias. PCA was subsequently applied to the residualized metrics. Forty-eight studies were included. The network showed a compact and highly interconnected structure, centered on motor and functional recovery concepts. PCA identified two principal components explaining of total variance. Relevant articles tended to occupy regions characterized by higher semantic integration and lower hierarchical influence. Although no clear categorical separation emerged, a consistent positional tendency was observed. These findings suggest that relevance may be represented as a topological property within a multidimensional semantic landscape, supporting the use of semantic-network approaches for literature screening and evidence synthesis.},
}

@article {pmid42678693,
year = {2026},
author = {Zhang, Q and Zhou, Q and Yang, M and Zou, S and Li, Y and Wang, Z},
title = {Non-pharmacological interventions for sleep improvement in Alzheimer's disease: A systematic review and meta-analysis.},
journal = {Journal of Alzheimer's disease : JAD},
volume = {},
number = {},
pages = {13872877261480066},
doi = {10.1177/13872877261480066},
pmid = {42678693},
issn = {1875-8908},
abstract = {BackgroundSleep disturbances are common in Alzheimer's disease (AD) and may worsen neuropsychiatric symptoms, caregiver burden, quality of life, and disease progression. Non-pharmacological strategies are increasingly used because long-term hypnotic or antipsychotic treatment may be limited by safety concerns, but their effects on subjective and objective sleep outcomes remain uncertain.ObjectiveTo evaluate the efficacy of non-pharmacological interventions for improving sleep in patients with AD.MethodsFollowing PRISMA guidelines, we searched PubMed, Embase, the Cochrane Library, Web of Science, and CINAHL through June 6, 2025, for randomized controlled trials of non-pharmacological interventions in AD. The primary outcome was the Pittsburgh Sleep Quality Index (PSQI); secondary outcomes were actigraphy-derived sleep efficiency, total sleep time, wake after sleep onset, number of awakenings, and time in bed. Standardized mean differences were pooled using fixed- or random-effects models. Subgroup, sensitivity, publication-bias, and meta-regression analyses were performed where appropriate.ResultsFourteen randomized controlled trials comprising 937 participants were included. Non-pharmacological interventions significantly reduced PSQI scores (SMD = -0.46, 95% CI -0.70 to -0.21). Neuromodulation-based interventions showed potentially favorable effects on PSQI, and caregiver-delivered programs such as NITE-AD modestly reduced nocturnal awakenings. No significant effects were observed for sleep efficiency, total sleep time, wake after sleep onset, or time in bed.ConclusionsNon-pharmacological interventions may modestly improve subjective sleep quality in AD, but objective sleep benefits remain limited. Larger, multicenter trials with standardized protocols, longer follow-up, harmonized subjective and objective outcomes, and AD-related biomarker assessment are needed.},
}

@article {pmid42678824,
year = {2026},
author = {Deng, W and Huang, L and Gao, T and Huang, S and Lu, R and Zhong, SH},
title = {Multi-Task EEG Diffusion Framework for Motor Functional Recovery in Stroke Patients.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3729675},
pmid = {42678824},
issn = {2168-2208},
abstract = {Stroke is one of the leading causes of long-term motor disability worldwide, placing a substantial burden on individuals, families, and healthcare systems. Innovative rehabilitation strategies such as motor imagery-based brain-computer interface (MI-BCI) are critical to accelerating stroke recovery. However, current MI-BCI methods face key challenges: low generalizability due to cross-patient variability, lack of effective functional assessment, limited availability of patient data, coupled with the lack of effective data augmentation approaches. To address these issues, we propose a unified EEG-based framework that simultaneously performs motor imagery classification, hemiplegic side detection, and functional recovery prediction. Our method introduces a diffusion model tailored to the spatio-temporal characteristics of EEG, incorporating a decoupled neural architecture with rotary spatial encoding and autoregressive temporal fusion. To mitigate data scarcity, we design two augmentation strategies specifically adapted to the characteristics of stroke EEG. Extensive experiments demonstrate superior performance and generalizability across multiple MI-BCI tasks, supporting the potential of the method for deployment in personalized stroke rehabilitation.},
}

@article {pmid42678835,
year = {2026},
author = {Kwon, J and Han, H and Hwang, J and Nam, S and Lee, T and Cha, HS and Ahn, KH and Im, CH},
title = {Hybrid Brain-Computer Interface for Controlling a Wearable Lower-Limb Exoskeleton with Augmented Reality Glasses for Gait Assistance.},
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.2026.3729803},
pmid = {42678835},
issn = {1558-0210},
abstract = {Conventional crutch-based control for lowerlimb exoskeletons often imposes a considerable physical burden and limits usability. To address these challenges, we developed a hybrid brain-computer interface (BCI) combining a steady-state visual evoked potential (SSVEP)- based BCI with asynchronous biosignal-based switches triggered by a wink and teeth clench. Practical usability was improved by implementing a wearable headband-type biosignal-recording device to acquire electroencephalography, electromyography, and electrooculography signals. Augmented reality glasses were used to present visual stimuli and gait guidance information. To support robust exoskeleton control in a wearable BCI environment, we proposed an asynchronous operational framework in which SSVEP responses were used for movement-mode selection, whereas wink- and clench-based switches were assigned to command execution and cancellation, respectively. Ten participants completed real-time walking experiments while wearing a custom lower-limb exoskeleton using both the conventional crutch-based and proposed control methods. The performance of the proposed system was evaluated using BCI classification accuracy and F1-scores for two asynchronous switches, whereas usability and workload were assessed using the system usability scale (SUS) and NASA task load index (NASA-TLX), respectively. Despite gross body movement during exoskeleton-assisted walking, the proposed hybrid control framework demonstrated robust mode selection, execution, and cancellation with an average SSVEP classification accuracy of 95.06%, F1-scores of 99.80% and 99.22% for the wink- and clench-based switches, respectively. Notably, only two false positive events were observed per switch across all participants. Furthermore, the proposed method exhibited a significantly higher SUS score than the crutch-based control method (78.25 vs. 53.50; p < 0.01) and a significantly lower physical demand in the NASATLX (3.15 vs. 7.85; p < 0.05), confirming its potential as a practical alternative. To the best of our knowledge, this is among the first studies in which a wearable hybrid SSVEPbased BCI for lower-limb exoskeleton operation applicable to real-world walking tasks was systematically demonstrated. Our findings suggest that the proposed hybrid BCI is a robust and less physically demanding alternative to a conventional control method, offering strong potential for daily assistance and gait rehabilitation.},
}

@article {pmid42678845,
year = {2026},
author = {Han, Z and Liu, Z and Liu, H and Peng, Y and Zhu, L and Kong, W and Cichocki, A},
title = {Contrastive Decoupling and Enhancement of Multi-view EEG Features for Imagined Speech Decoding.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3729643},
pmid = {42678845},
issn = {1558-2531},
abstract = {Imagined speech decoding remains challenging in brain-computer interfaces (BCIs) due to the low signal-to-noise ratio and complex spatio-temporal-spectral structure of Electroencephalogram (EEG) data. Existing studies mainly rely on single-view features or simple fusion strategies, limiting their ability to capture diverse neural characteristics during speech imagery. To address this limitation, we propose a Multi-view Feature Contrastive Decoupling and Enhancement (MFCDE) framework that integrates multi-view feature construction, feature decoupling, and adaptive masking. Four complementary views, including temporal, frequency-domain, phase-locking value (PLV), and graph-theoretic features, are extracted to characterize speech imagery-related neural dynamics. The decoupling mechanism reduces cross-view redundancy while preserving the discriminative information of each view. Experiments show that MFCDE consistently outperforms existing baselines in classification performance and stability. The learned view-shared and view-specific representations further provide neurophysiological insights by revealing the complementary contributions of temporal, spectral, and connectivity-based EEG patterns to imagined speech discrimination, indicating that reliable decoding depends on the joint utilization of neural dynamics, oscillatory activity, and inter-regional functional interactions.},
}

@article {pmid42678872,
year = {2026},
author = {Zhao, Z and Cao, Y and Yu, H and Yu, H and Huang, J},
title = {U2Multi-UDA: A Unified Multilevel Multisource Unsupervised Domain Adaptation Method for Motor Imagery.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2026.3713085},
pmid = {42678872},
issn = {2162-2388},
abstract = {Motor imagery (MI) is a core paradigm in Brain-computer interface (BCI) research, but its practical application remains limited by intersubject variability and the scarcity of labeled target-domain data. Existing methods usually focus on a single adaptation level, such as domain alignment, feature interaction, or model fine-tuning, which limits comprehensive cross-domain adaptation (DA). To address this issue, this study proposes U2Multi-UDA, a unified multilevel multisource unsupervised DA framework for MI decoding. U2Multi-UDA integrates these three adaptation levels into a single pipeline. First, optimal transport (OT) aligns source and target distributions, while mutual information estimates source-domain relevance weights to characterize the contribution of each source domain to the target domain. Second, spatio-temporal electroencephalography (EEG) features are extracted and fused through multisource cross-attention, where the source-domain relevance weights guide cross-domain feature fusion, and pseudolabels enhance target-domain feature learning. Finally, segmented weight-decomposed low-rank adaptation (DoRA) enables parameter-efficient target-domain fine-tuning while reducing overfitting. Experiments on BCI Competition IV 2a, BCI Competition IV 2b, and the self-constructed MI-GS dataset show that U2Multi-UDA improves mean accuracy by 2.69, 1.89, and 3.83 percentage points, respectively, over the best-performing baselines, with consistent gains in Kappa values. Ablation and sensitivity analyses further confirm the effectiveness, robustness, and physiological plausibility of the proposed framework.},
}

@article {pmid42680783,
year = {2026},
author = {Shi, C and Nuttin, L and Gao, Z and He, Y and Russo, P and Liaw, HP and Gourdouparis, M and Lambrechts, D and Dolmans, G and Liu, YH},
title = {An Event based Body Coupled Transdural Telemetry for Intracortical Brain Computer Interfaces.},
journal = {Communications engineering},
volume = {5},
number = {1},
pages = {},
pmid = {42680783},
issn = {2731-3395},
abstract = {Intracortical brain-computer interfaces (iBCIs) hold promise for restoring motor, sensory, and cognitive functions, including applications in paralysis treatment and speech decoding. High-density microelectrode arrays (MEAs) provide fine spatial and temporal discrimination of neural activity, but the considerable upsurge in data generation poses challenges for wireless transmission from miniaturized implants due to constraints on power, bandwidth, heat dissipation, and device size. To address these constraints, we propose a two-stage wireless iBCI architecture comprising a transdural galvanic-coupled body channel communication (BCC) link from a free-floating MEA to an intracranial unit, followed by a transcutaneous link to an external unit. This study focuses on the transdural BCC telemetry system, which provides compact, wideband, and energy-efficient data transmission. Phantom tests, and ex vivo experiments using a human cadaveric head specimen validate the system, demonstrating wireless transmission up to 500 Mbps with 20% duty cycling and bit error rates below 10[-5]. The system incorporates the send-on-delta encoder (SODA), achieving up to 11.4× data compression and reducing thermal load for meeting the safety guidelines. Safety is further examined using brain-on-a-chip models, which demonstrate that the system does not evoke unintended neural activity, supporting the platform's long-term viability for high-resolution iBCIs.},
}

@article {pmid42681934,
year = {2026},
author = {Wu, H and Yang, Y and Liu, C and Ge, Y and Zhang, J and Wang, B and Ren, X and An, R and Quan, Y and Li, Z and Zhao, L and Ren, W and Niu, G},
title = {Flexible Microneedle Array Electrode with Improved Comfort, Low Impedance, and High Performance for Electrophysiological Recording.},
journal = {Small (Weinheim an der Bergstrasse, Germany)},
volume = {},
number = {},
pages = {e75417},
doi = {10.1002/smll.75417},
pmid = {42681934},
issn = {1613-6829},
abstract = {High-fidelity electrophysiological recording is critical for wearable brain-computer interfaces and human-machine interaction. However, balancing signal quality and wearing comfort remains a challenge: wet electrodes suffer from gel dehydration, whereas conventional dry electrodes often exhibit high impedance and mechanical instability. Here, we present a flexible microneedle array (fMNA) electrode fabricated using a scalable micro-electro-mechanical system process. The electrode comprises octagonal pyramidal silicon microneedles coated with Au/Cr on a flexible parylene substrate, providing high conductivity, mechanical robustness, and conformal scalp contact. Integrated with a specialized denoising algorithm, the fMNA achieved a contact impedance of 7.6 kΩ@10 Hz, 1-2 orders of magnitude lower than that of commercial wet electrodes. It also exhibited excellent durability and flexibility, with a minimum bending radius of 3 mm. Its recording performance was systematically validated through electroencephalographic visual evoked potential and cognitive engagement experiments, together with electrooculography. Compared with wet electrodes, the fMNA produced 41%-46% higher signal amplitudes, a higher signal-to-noise ratio, improved stability, lower impedance drift, and fewer motion artifacts. These results establish the fMNA as a versatile platform for high-quality electrophysiological recording and long-term wearable bioelectronic monitoring.},
}

@article {pmid42682360,
year = {2026},
author = {Ye, F and Xu, J and Liu, H},
title = {Functional substitution and long-term dependency in BCI-FES-based neurorehabilitation.},
journal = {International journal of surgery (London, England)},
volume = {112},
number = {7},
pages = {13297-13298},
pmid = {42682360},
issn = {1743-9159},
}

@article {pmid42682605,
year = {2026},
author = {Liu, G and Zheng, Y and Zhang, J and Tian, L and Zhou, W},
title = {Editorial: Advances in explainable analysis methods for cognitive and computational neuroscience.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1938732},
pmid = {42682605},
issn = {1662-4548},
}

@article {pmid42684150,
year = {2026},
author = {Yang, K and Hu, Y and Zheng, R and Xu, J and Wang, N and Zhang, J and Peng, Y and Kong, W},
title = {Multi-branch heterogeneous network of exploiting complementary multi-view features for decoding finger motor imagery EEG.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-15},
doi = {10.1080/10255842.2026.2726433},
pmid = {42684150},
issn = {1476-8259},
abstract = {In contrast to general motor imagery involving large body parts, research on finger motor imagery is very scarce. Due to more refined motor functions, the decoding of finger motor imagery is more arduous and has lower accuracy than that of general motor imagery. In order to improve the decoding accuracy of finger motor imagery, this paper proposes the problem of identifying complementary multi-view decoding features and the problem of electrode channel difference of convolution kernels. A novel multi-branch heterogeneous network (MBHN) consisting of three groups of diversified branches is proposed to effectively extract and exploit complementary features of raw-view, frequency-decomposition-view and wavelet-view. Moreover, the channel adaptive kernel (CAK) module is proposed as a solution to the problem of channel difference of convolution kernels. The experimental results on the public finger motor imagery dataset show that our MBHN model achieves the state-of-the-art decoding accuracy of 58.49%. Additionally, the integration of the auxiliary supervision mechanism and the hybrid loss function is a very effective approach to fully leverage the complementarity of multi-view deep features. Our code is publicly available at https://github.com/ykhdu/MBHN.},
}

@article {pmid42670087,
year = {2026},
author = {Wang, S and Qin, F and Pan, Q and Liu, C and Zhu, H and Li, W and Yan, H and Huang, W},
title = {Enhancing Neural Encoding of Natural Scenes through Hierarchical Integration of Saliency and Semantic Context.},
journal = {International journal of neural systems},
volume = {},
number = {},
pages = {2750020},
doi = {10.1142/S0129065727500201},
pmid = {42670087},
issn = {1793-6462},
abstract = {Understanding how the human brain encodes complex natural scenes remains a central problem in computational neuroscience and artificial intelligence. Existing visual encoding models often rely on a single dominant feature representation and may insufficiently characterize how saliency-guided spatial information and high-level semantic context jointly contribute to cortical response prediction. To address this issue, this study proposes a saliency-guided multimodal visual encoding model, termed SMG-MVEM, to predict voxel-wise cortical responses to natural scene stimuli. The model integrates image features, saliency cues, and text-derived semantic representations through a hierarchical fusion architecture, followed by a Transformer-based brain mapper. Experiments on the Natural Scenes Dataset (NSD) show that SMG-MVEM improves prediction performance over representative neural encoding baselines and internal control variants, with the average PCC increasing from [Formula: see text] for the best-performing baseline to [Formula: see text]. Regional analyses further show that saliency contributed more strongly to early visual areas, whereas semantic features provided greater benefits in higher-order regions. Representational analyses also suggest that the model-predicted responses preserved aspects of hierarchical and category-related organization across the visual cortex. These findings indicate that structured integration of saliency and semantic context can improve cortical response prediction and provide interpretable representational patterns for natural vision.},
}

@article {pmid42670213,
year = {2026},
author = {Long, Y and Zhou, S and Zou, G and Liu, J and Xu, J and Zou, Q and Gao, JH},
title = {EEG Microstates and Related Brain Networks During Light Sleep.},
journal = {Journal of sleep research},
volume = {},
number = {},
pages = {e70437},
doi = {10.1111/jsr.70437},
pmid = {42670213},
issn = {1365-2869},
support = {2021ZD0200800//STI2030-Major Projects/ ; 2021ZD0200500//STI2030-Major Projects/ ; 2021ZD0200506//STI2030-Major Projects/ ; 2022ZD0206000//STI2030-Major Projects/ ; L2602028//Beijing Natural Science Foundation/ ; w2431053//National Natural Science Foundation of China/ ; 82327806//National Natural Science Foundation of China/ ; 82372034//National Natural Science Foundation of China/ ; 82001911//National Natural Science Foundation of China/ ; 25PJC076//Shanghai Pujiang Program/ ; 2024DSYL055//Shanghai International Studies University Academic Mentorship Program Project/ ; },
abstract = {Light sleep, including Stages N1 and N2, constitutes more than half of total human sleep duration. It serves essential functions in transitioning from wakefulness to deep sleep and in memory processing. Previous studies have identified four consistent electroencephalogram (EEG) microstates during wakefulness and sleep. Simultaneous EEG and functional magnetic resonance imaging (fMRI) studies have shown that EEG microstates are associated with specific brain functional networks during wakefulness and slow wave sleep. However, the relationship between microstates and brain networks during light sleep remains unexplored. To address this gap, simultaneous EEG-fMRI data acquired during light sleep were used to examine the correspondence between microstates and brain networks. The EEG microstate informed fMRI analysis revealed that Microstate C was associated with the cerebellum, and Microstate D was associated with the thalamus and motor areas during both N1 and N2 sleep. No significant results were found in Microstate A or B during N1. Additionally, linear mixed-effect analysis verified that Microstate D's association with the motor network and thalamus persisted during both N1 and N2, though their activity showed opposing trends between stages: Microstate D-related thalamic activity was lower in N1 than in N2, whereas the motor cortex exhibited the opposite pattern. These findings highlight distinct relationships between EEG microstates and brain networks during N1 and N2 sleep and implicate the thalamus and motor cortex as key neural substrates during light sleep.},
}

@article {pmid42670825,
year = {2026},
author = {Branco, MP and Kinney-Lang, E and Ruest, N and Carlson, HL and Kirton, A and Vansteensel, MJ},
title = {Creating a Roadmap for Pediatric Implanted Brain-Computer Interfaces.},
journal = {Neurorehabilitation and neural repair},
volume = {},
number = {},
pages = {15459683261481764},
doi = {10.1177/15459683261481764},
pmid = {42670825},
issn = {1552-6844},
abstract = {BACKGROUND: Implanted brain-computer interfaces (iBCIs) can record signals directly from the brain and translate them into computer commands continuously, at high speed and fidelity. Over 150 people worldwide have been implanted with an iBCI, and this number is expected to increase rapidly as iBCIs become commercially available. Despite the progress that is being made in the development of safe, wireless, and highly effective iBCIs, none of these have been implemented in youth or adults with pediatric-onset conditions.

OBJECTIVE: Pediatric-onset conditions, such as Cerebral Palsy (CP), represent a large proportion of the global burden of complex and severe disability. Since affected individuals, particularly youth, will likely benefit significantly from iBCIs, they should not be left behind in technological progress that would be life-changing. We outlined the evidence gaps, steps required, and arguments for greater focus on this population in iBCI research and development.

METHODS: Here, we present the result of two years of cumulative effort, combining expert opinions and findings from multiple transdisciplinary engagement sessions, including the first International Virtual Summit on Implanted BCIs for Children with Complex Needs, follow-up themed workgroup sessions, and a final in-person workshop held at the 11th International BCI Society Meeting.

RESULTS: We established a world-first visionary, community-and-partner-engaged roadmap for the design, development, and implementation of iBCIs for youth with CP to meaningfully interact with the world.

CONCLUSIONS: Developing iBCI systems for youth with CP requires a fundamental shift toward child‑centric neuroscience, engineering, and user‑driven design rather than adapting adult‑oriented technologies.},
}

@article {pmid42671112,
year = {2026},
author = {Chen, YR and Li, PX and Zhang, HF and Liu, XY and Zhang, JY},
title = {Comparative Effects of Monocular and Stereopsis Training on Visual Acuity and Stereoacuity in Children With Amblyopia.},
journal = {Investigative ophthalmology & visual science},
volume = {67},
number = {10},
pages = {69},
doi = {10.1167/iovs.67.10.69},
pmid = {42671112},
issn = {1552-5783},
mesh = {Humans ; *Amblyopia/physiopathology/therapy ; Child ; *Visual Acuity/physiology ; *Depth Perception/physiology ; Female ; Male ; Vision, Binocular/physiology ; Adolescent ; *Vision, Monocular/physiology ; },
abstract = {PURPOSE: To evaluate changes in visual acuity (VA) and stereoacuity in children with amblyopia following monocular or stereopsis training delivered alongside standard clinical care and to examine factors associated with training-related improvements in visual outcomes.

METHODS: Forty-three children with amblyopia (7.3-14.8 years; 37 anisometropic, five strabismic, and one mixed) completed approximately 29 sessions of either monocular training (grating acuity or motion discrimination targeting the amblyopic eye, n = 23) or binocular stereopsis training (dichoptic disparity discrimination, n = 20). All participants continued standard clinical management, including optical correction and prescribed patching. Pre- and post-training assessments included amblyopic-eye VA, interocular acuity difference (IOD), and Randot stereoacuity.

RESULTS: Both training protocols produced significant task-specific learning. Amblyopic-eye VA improved significantly in both groups, with no evidence of differential effects between training approaches. Stereoacuity also improved in both groups, with significantly greater gains following stereopsis training than monocular training. Exploratory analyses indicated that poorer baseline amblyopic-eye VA was associated with larger VA gains; however, this association did not survive correction for multiple comparisons. Stereoacuity improvement was not significantly associated with baseline VA, IOD, age, or changes in VA.

CONCLUSIONS: Visual training combined with standard clinical care improved amblyopic-eye VA and stereoacuity in children with amblyopia. Although both training approaches yielded comparable gains in VA, stereopsis training produced larger improvements in stereoacuity, supporting its potential role in addressing residual binocular deficits after conventional treatment.},
}

Humans
*Amblyopia/physiopathology/therapy
Child
*Visual Acuity/physiology
*Depth Perception/physiology
Female
Male
Vision, Binocular/physiology
Adolescent
*Vision, Monocular/physiology

@article {pmid42671286,
year = {2026},
author = {Murray, S},
title = {Self-Trust as a Unifying Principle for the Dimensions of Agency in Neurotechnology.},
journal = {AJOB neuroscience},
volume = {},
number = {},
pages = {1-6},
doi = {10.1080/21507740.2026.2716034},
pmid = {42671286},
issn = {2150-7759},
abstract = {Neurotechnologies such as brain-computer interfaces (BCIs) and deep brain stimulation (DBS) raise distinctive concerns about human agency. A recent proposal by Schönau et al. identifies four dimensions along which neurotechnologies may threaten agency: responsibility, privacy, authenticity, and trust. The Schönau et al. framework valuably maps how neurotechnologies affect users and has inspired qualitative assessment tools. Yet although it acknowledges interconnections among the four dimensions, it does not explain what structurally unifies them. This represents a key gap in the view. Assessment instruments modeled on the framework, such as their Qualitative Agentive Competency Tool (Q-ACT), evaluate each dimension independently, risking fragmented assessments that miss the integrated nature of agential harm. I argue that agential self-trust unifies these four dimensions. Each tracks a distinct way that neurotechnologies can erode the self-trust constitutive of planning agency: confidence in one's control over action (responsibility), in the boundaries of one's deliberative life (privacy), in one's psychological continuity (authenticity), and in one's sensory and evaluative capacities (trust). This carries concrete policy implications. Current informed consent procedures for neurotechnology trials enumerate risks along separate dimensions without flagging the cumulative threat to a user's capacity for agential self-trust. Likewise, assessment instruments should include integrative measures that track agential self-trust across domains. As neurotechnology governance develops at the international level, a unified account of agential harm within this dimensional framework can guide both consent design and longitudinal monitoring.},
}

@article {pmid42671376,
year = {2026},
author = {Gardiner, CA and Atlas, MD and Eikelboom, RH},
title = {Bone conduction implants in Australia over two decades.},
journal = {Cochlear implants international},
volume = {},
number = {},
pages = {1-9},
doi = {10.1080/14670100.2026.2724210},
pmid = {42671376},
issn = {1754-7628},
abstract = {BACKGROUND: Bone conduction hearing implants were introduced in the late 1970s and have since undergone multiple iterations in mechanical and electronic design, as well as surgical requirements. While there have been reports of hearing outcomes and complications, limited population-level data exist. This study examined the number of bone conduction implant (BCI) procedures in Australia over 20 years (July 2004 to June 2024), exploring age, sex and temporal factors.

METHODS: Procedural data were obtained from the Australian Institute of Health and Welfare (AIHW) and population data from the Australian Bureau of Statistics (ABS). The number of procedures was categorised by year, age group, sex, and adjusted for the population.

RESULTS: There were a total of 4536 BCI implants over the 20-year period, and 118 BCI explants during the last five reporting periods. There was a gradual increase in BCI procedures from 2004 to 2005, peaking at 434 procedures in 2016-2017, followed by a slow decline to 2022-2023, with a noticeable dip in 2021-2022, which coincided with the COVID-19 pandemic. The 60-79 year age group underwent the highest number of procedures across all periods investigated, followed by the 40-59 year age group. Minimal sex differences were observed between males and females; however, overall, females aged 40-59 years had more BCI procedures than males of the same age group.

DISCUSSION: This study provides valuable insights into BCI procedures in Australia, supporting future healthcare planning and policy development.},
}

@article {pmid42675986,
year = {2026},
author = {Wang, Y and He, Y and Cheng, Z and Zhang, Y and Yan, W and Xu, D and Liu, S and Zhao, Q and Xu, X},
title = {Low-Temperature Perovskite Crystallization Suppresses Interfacial Thermal Stress for Robust and High-Resolution Flat-Panel X-Ray Imaging.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e74876},
doi = {10.1002/adma.74876},
pmid = {42675986},
issn = {1521-4095},
support = {62205154//National Natural Science Foundation of China/ ; 62288102//National Natural Science Foundation of China/ ; TNMPP-2025-03//CIRP Open Fund of Radiation Protection Laboratories/ ; KYCX25_1183//Postgraduate Research & Practice Innovation Program of Jiangsu Province/ ; NY221112//Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications/ ; },
abstract = {Interfacial thermal stress is a common issue limiting the performance of perovskite optoelectronics, particularly x-ray detectors. This challenge originates from thermomechanical incompatibility: conventional high-temperature perovskite crystallization induces severe interfacial thermal stress upon cooling, triggering film delamination and cracking. Herein, through solvent engineering and intermediate design, we demonstrate, for the first time, the low-temperature formation of perovskite thick films, mitigating this issue at its origin. This is achieved by a new perovskite ink formulation containing highly volatile 2-methoxyethanol (2-ME) and monodentate coordinating 1-cyclohexyl-2-pyrrolidone (CHP). This combination yields a new intermediate, (CHP)2Pb3I6, that largely decouples nucleation from growth and lowers the crystallization temperature from 135 to 75°C. This reduction, along with an improved wettability, decreases the interfacial thermal stress by ∼75% and doubles the interfacial adhesion strength. Consequently, robust integration of perovskite thick films with an indium-gallium-zinc-oxide (IGZO) thin-film transistor (TFT) backplane is achieved. The resulting flat-panel imager delivers a spatial resolution of 4.73 lp mm[-1] (0.59 lp pix[-1]) when the modulation transfer function (MTF) reaches 0.2, outperforming commercial amorphous selenium (α-Se) and previous perovskite-based imagers. This work elucidates intermediate-regulated crystallization thermodynamics and opens avenue for integrating monolithic perovskite optoelectronics onto temperature‑sensitive substrates.},
}

@article {pmid42661845,
year = {2026},
author = {Kubaščík, M and Aggarwal, S and Karpiš, O and Tupý, A and Šarafín, P and Chochul, M},
title = {EEG-based hypoglycemia detection in Type 1 Diabetes: Proof-of-concept study.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1852403},
pmid = {42661845},
issn = {1662-5161},
abstract = {Early detection of hypoglycemia among non-hypoglycemic conditions is critical in type 1 diabetes (T1D), as delayed intervention can lead to serious neurological and metabolic consequences. Although continuous glucose monitoring (CGM) systems provide continuous glucose measurements, they are invasive and do not capture early neurophysiological alterations associated with hypoglycemia-related metabolic changes. This study presents a preliminary feasibility investigation of a non-invasive brain-computer interface approach for classifying hypoglycemia versus non-hypoglycemia in individuals with type 1 diabetes using electroencephalography (EEG). The novelty lies in demonstrating EEG-based hypoglycemia detection (binary classification) under data-limited conditions using optimized segmentation and lightweight classifiers. Additionally, EEG characteristics were compared with recordings from five healthy controls to provide a baseline reference for spectral activity patterns. EEG recordings from participants with T1D were synchronized with continuous glucose monitoring (CGM) data and pre-processed using a 0.5-50 Hz band-pass filter. The control group underwent identical EEG pre-processing without CGM synchronization. Reproducible spectral patterns were identified: hypoglycemia was associated with characteristic delta and beta alterations, while changes observed outside hypoglycemia were less consistent and did not support reliable separation within the non-hypoglycemic class. Multiple segmentation strategies and data-efficient machine learning models were evaluated under limited data conditions. Classical classifiers demonstrated promising within-subject performance under severely data-limited conditions, with Quadratic Discriminant Analysis (QDA) achieving the best results (accuracy 0.96212, macro-F1 0.96201) for binary classification focused on hypoglycemia detection. Confusion matrix analysis indicated a low rate of clinically relevant misclassifications. These findings support the feasibility of lightweight, real-time, non-invasive EEG-based systems for early hypoglycemia detection.},
}

@article {pmid42663733,
year = {2026},
author = {Kritika, M},
title = {Cognitive Sovereignty: An AI-Aware Governance Framework for Neural Data Threats, Autonomous Cyber Defense, and Identity-Aware Security in Neurotechnology Systems.},
journal = {Neuroinformatics},
volume = {24},
number = {3},
pages = {},
pmid = {42663733},
issn = {1559-0089},
mesh = {Humans ; *Artificial Intelligence ; *Brain-Computer Interfaces ; *Computer Security/legislation & jurisprudence ; *Cognition/physiology ; },
abstract = {Neural data collected using brain-computer interfaces, neural implants, and emotion detection systems is analyzed by AI classifiers and agentic architectures to serve purposes such as authentication, access control, and behavioral inference, however, there exists no comprehensive, binding cybersecurity or data protection regime to regulate such neural data. The regulations that currently exist i.e., GDPR, HIPAA, the Budapest Convention, the 2025 UNESCO Recommendation on Neurotechnology Ethics, and a small number of state laws (e.g., Colorado 2024, California SB 1223, Montana, Connecticut) create a fragmented and incomplete emerging framework rather than no framework at all. In this paper, the author propose Cognitive Sovereignty architecture, an approach of governance through the combination of a legally recognized definition and technical parameters defining neural data as a new class of data which necessitates specific regulatory, adversarially sound processing frameworks, and jurisdictionally agnostic enforcement mechanisms. By conducting comparative law research, threat modeling based on STRIDE model and governance modeling, this paper highlights structural issues with the existing regimes and suggests a framework composed of Declaration on Cognitive Sovereignty, neuro-cybercrime protocol of the Budapest Convention, and AI layer-specific compliance requirements based on NIST AI RMF and the EU AI Act.},
}

Humans
*Artificial Intelligence
*Brain-Computer Interfaces
*Computer Security/legislation & jurisprudence
*Cognition/physiology

@article {pmid42664331,
year = {2026},
author = {Yang, L and Lyu, M and Wang, YJ and Xie, X and Zhen, Z and Lai, JH and Zhang, RY},
title = {Mental bootstrapping enables human-level concept learning in self-supervised deep models.},
journal = {Science advances},
volume = {12},
number = {35},
pages = {eaea7202},
pmid = {42664331},
issn = {2375-2548},
mesh = {Humans ; Artificial Intelligence ; Algorithms ; *Concept Formation ; *Supervised Machine Learning ; *Learning ; *Deep Learning ; },
abstract = {How agents acquire abstract concepts from sparse, diverse examples-often without explicit supervision-remains a central problem in cognitive science and artificial intelligence. Human studies suggest that this ability depends on mental bootstrapping, the gradual construction of complex concepts from simpler partial structures. Building on this idea, we develop a self-supervised framework that trains models on systematically simplified versions of abstract reasoning tasks containing incomplete but structured concept cues. This algorithm enables models to form internal abstractions under limited resources and later apply them to more complex problems. We evaluate the framework across 12 abstract visual reasoning datasets testing in-distribution concept induction, out-of-distribution generalization, and few-shot learning. To contextualize performance, we also measure human accuracy on the same tasks. Models trained on simplified problems generalize robustly, reaching or even surpassing human-level performance. These findings show that abstract reasoning can emerge from structured simplification and minimal data, offering a computational account of concept learning in humans and machines.},
}

Humans
Artificial Intelligence
Algorithms
*Concept Formation
*Supervised Machine Learning
*Learning
*Deep Learning

@article {pmid42666513,
year = {2026},
author = {Dai, J and Zhu, L and Babiloni, F and Kong, W},
title = {A balanced multimodal decoding framework of EEG and fNIRS for motor imagery.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {165},
pmid = {42666513},
issn = {1871-4080},
abstract = {Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) exhibit complementary advantages in temporal and spatial resolution for brain activity monitoring, and their integration has the potential to improve motor imagery (MI) decoding performance. However, in EEG-fNIRS multimodal MI decoding, modality heterogeneity and differences in signal-to-noise ratio and convergence speed often cause joint training to bias toward the modality that is easier to learn, leading to modality dominance, degraded fused representations, and reduced generalization. To address this issue, we propose a dynamic re-initialization framework for EEG-fNIRS multimodal decoding, which achieves cross-modal balanced learning through a diagnosis-adjustment-re-initialization mechanism. The proposed method uses the separability difference of unimodal features between the training and validation sets as a diagnostic signal, and integrates network hierarchical priors with modality-specific gradient statistics to derive adjustment factors. Guided by these signals, the framework periodically performs soft re-initialization of the EEG and fNIRS encoder parameters during training, promoting re-learning of weaker modalities, suppressing single-modality dominance, and preserving the stability of converged representations. As a result, multimodal imbalance is alleviated and the discriminative capability of fused representations is enhanced. Experiments on a publicly available EEG-fNIRS motor imagery dataset demonstrate that the proposed method achieves an average classification accuracy of 92.42 ± 4.54%, significantly outperforming both unimodal and conventional fusion baselines and showing strong cross-subject consistency.},
}

@article {pmid42668603,
year = {2026},
author = {Mo, F and Zhao, X and Xu, Y and Tang, C and Zhang, D and Li, S and Chen, D and Li, W and Song, Z and He, S},
title = {Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.},
journal = {iScience},
volume = {29},
number = {9},
pages = {117268},
pmid = {42668603},
issn = {2589-0042},
abstract = {Memory prosthetics, closed-loop brain-computer interfaces that decode hippocampal activity and deliver adaptive stimulation, are transitioning from animal proof-of-concept to first-in-human trials. Realizing chronically implantable systems requires co-design of three materials-mediated subsystems whose structure-property-processing (SPP) relationships have been treated in isolation: biocompatible electrode interfaces, on-chip neuromorphic computation, and closed-loop control hardware. This review presents an integrated framework. We map neuroscientific findings (theta-phase tracking, theta-gamma coupling, sharp-wave ripple detection) onto engineering specifications for latency, sampling, and charge injection, and onto materials requirements for impedance, switching endurance, and chronic stability. We develop an SPP taxonomy of two dominant materials families: chronic electrode coatings (Pt-Ir, IrOx, PEDOT:PSS, carbon-based, MXene) and oxide memristive synapses (Al2O3/TiO2-x, SrTiO3, HfO2). We further distinguish established findings from emerging directions and flag where small-cohort clinical results have been over-generalized. This synthesis provides materials-design targets for next-generation memory-prosthetic hardware.},
}

@article {pmid42668878,
year = {2026},
author = {Ankireddypalli, S and Avadhani, D and Roy, MS and Kumar, Y and Prajapati, TT and Vyas, N and Jadhav, RS},
title = {Effectiveness of Neurological Physiotherapy Interventions in Post-stroke Motor Rehabilitation: A Systematic Review.},
journal = {Cureus},
volume = {18},
number = {7},
pages = {e113639},
pmid = {42668878},
issn = {2168-8184},
abstract = {Stroke frequently results in persistent upper-limb, lower-limb, and gait impairments that restrict independence and quality of life. Neurological physiotherapy aims to enhance post-stroke motor recovery through task-specific practice, strengthening, sensory-motor retraining, feedback, neuroplasticity stimulation, and activity-based rehabilitation. This systematic review aimed to evaluate the effectiveness of neurological physiotherapy interventions in post-stroke motor rehabilitation and to summarize intervention characteristics, outcomes, methodological quality, and risk of bias. A systematic search was conducted across electronic databases, including PubMed, Google Scholar, ScienceDirect, and the Cochrane Library, to identify randomized controlled trials (RCTs) published from 2015 onward and available as full-text articles. After duplicate removal and screening, 47 full-text articles were assessed, and 11 RCTs were included. Data were extracted on study design, participants, stroke phase, intervention type, comparator, outcomes, numerical findings, and adverse events. Findings were synthesized narratively due to heterogeneity in intervention type, recovery phase, treatment dose, and outcome measures; no meta-analysis was performed. The review included functional strength training (FST), movement performance therapy, virtual reality (VR), robot-assisted rehabilitation, action observation therapy, mirror therapy, electrical stimulation, brain-computer interface (BCI)-assisted therapy, and walking-based interventions. Most interventions improved motor or functional outcomes, although superiority over conventional or dose-matched therapy was inconsistent. Overall, individualized, progressive, task-oriented neurological physiotherapy remains central to post-stroke motor recovery, with technology-assisted approaches offering additional value when integrated with meaningful functional practice.},
}

@article {pmid42661225,
year = {2026},
author = {Xu, B and Guo, Z and Chen, J and Xie, Y and Wang, P and Xiong, X and Yu, J and Xu, Z and Fu, Y and Lan, Z and Peng, G and Zhang, J},
title = {HSPA8 orchestrates SNARE complex assembly to drive extracellular vesicle-mediated spread of p-tau217 in Alzheimer's disease.},
journal = {Translational neurodegeneration},
volume = {15},
number = {1},
pages = {},
pmid = {42661225},
issn = {2047-9158},
support = {LY24H090006//Natural Science Foundation of Zhejiang Province/ ; 2025C01120//Key Research and Development Program of Zhejiang Province/ ; 2024C03098//Key Research and Development Program of Zhejiang Province/ ; 82571348//National Natural Science Foundation of China/ ; 32530027//National Natural Science Foundation of China/ ; },
mesh = {*Alzheimer Disease/metabolism/pathology ; Animals ; Humans ; *tau Proteins/metabolism ; *Extracellular Vesicles/metabolism ; *HSC70 Heat-Shock Proteins/metabolism ; *SNARE Proteins/metabolism ; Mice ; Hippocampus/metabolism ; Male ; Amyloid beta-Peptides/metabolism ; },
abstract = {BACKGROUND: Dysregulation of multivesicular bodies (MVBs) in Alzheimer's disease (AD) contributes to aberrant tau secretion via extracellular vesicles (EVs). This may potentially explain our previous paradoxical observation of elevated free-form p-tau217 alongside reduced p-tau217[+] EVs in plasma. This study aimed to investigate the mechanisms underlying the reduction of p-tau217[+] EVs to uncover AD therapeutic targets.

METHODS: By integrating hippocampal spatial transcriptomics of human brain with EV proteomics of cerebrospinal fluid, we identified key regulators of p-tau217[+] EV release. Subsequently, we investigated the mechanisms underlying the synthesis and secretion of p-tau217[+] EVs. The regulatory roles of these candidate proteins were systematically evaluated through shRNA knockdown and interference with a synthetic peptide in both Aβ42-treated cells and AD model mice.

RESULTS: Heat shock protein family A member 8 (HSPA8) was identified as a crucial regulator of EV biogenesis and release, mediating the Aβ-SNAP29 interaction to disrupt SNARE complex assembly and impair p-tau217[+] EV secretion. In AD models, HSPA8 inhibition with shRNA rescued p-tau217[+] EVs and improved cognitive function. Additionally, blocking the Aβ-SNAP29 interaction with a selective peptide inhibitor for HSPA8 reversed the decline in p-tau217[+] EV and cognitive deficits.

CONCLUSIONS: These findings reveal a role of HSPA8 in regulating the MVB-mediated EV release and tau propagation, and highlight HSPA8 as a promising therapeutic target for modifying AD progression.},
}

*Alzheimer Disease/metabolism/pathology
Animals
Humans
*tau Proteins/metabolism
*Extracellular Vesicles/metabolism
*HSC70 Heat-Shock Proteins/metabolism
*SNARE Proteins/metabolism
Mice
Hippocampus/metabolism
Male
Amyloid beta-Peptides/metabolism

@article {pmid42661532,
year = {2026},
author = {Vyhnánková, B and Džupa, V and Šubrt, Z},
title = {[Sternal Fracture as a Risk Factor for Blunt Cardiac Injury].},
journal = {Acta chirurgiae orthopaedicae et traumatologiae Cechoslovaca},
volume = {93},
number = {3},
pages = {178-188},
pmid = {42661532},
issn = {0001-5415},
mesh = {Humans ; *Sternum/injuries ; *Wounds, Nonpenetrating/diagnosis/etiology/complications ; *Fractures, Bone/complications/diagnosis ; Risk Factors ; *Heart Injuries/etiology/diagnosis ; *Myocardial Contusions/etiology/diagnosis ; },
abstract = {Motor vehicle collisions are the leading cause of blunt chest trauma, including blunt cardiac injury (BCI) and sternal fracture (SF). Blunt cardiac injury occurs in about 20% of blunt chest trauma reaching to 76% in polytrauma patients. Reported SF rates range from 1.6% to 42%. This review evaluates the link between SF and BCI with focus on incidence, diagnosis and implications for patient management. A systematic search of the scientific literature in online databases over the past 15 years was conducted. The final set included 31 studies, predominantly retrospective, which were subsequently analyzed with emphasis on the definition of BCI, its occurrence and its association with SF. BCI refers to a heterogeneous group of cardiac injuries resulting from a common traumatic mechanism. The term is often used interchangeably with myocardial contusion, thereby limiting the comparability of study results. Clinically significant injury can range from mild arrhythmias to cardiogenic shock, while some cases of BCI may remain completely asymptomatic. SF is common and indicates a substantial force to the chest. Patients are often categorized into two groups: those with an isolated sternal fracture, which is usually a benign injury without cardiac involvement, and those with a combined sternal fracture, which is common in polytrauma patients. The association between SF and BCI remains controversial. Some studies regard SF as a general indicator of overall trauma severity, whereas others identify it as an independent risk factor for BCI. There are multiple approaches in diagnostics of BCI. Initial screening includes electrocardiography and monitoring of troponin I levels. If both are within normal limits, BCI is unlikely. Echocardiography is recommended for hemodynamically unstable patients or when initial test results are abnormal. Management includes continuous ECG monitoring, treatment of arrhythmias and analgesia. Current recommendations support screening for BCI following blunt chest trauma. Evidence is mixed on whether SF alone predicts the presence of BCI. Further prospective studies using a uniform definition of BCI are required to confirm the clinical significance of SF in the diagnosis and patient prognosis. Physicians should maintain a high index of suspicion for BCI in patients after high-energy chest trauma.},
}

Humans
*Sternum/injuries
*Wounds, Nonpenetrating/diagnosis/etiology/complications
*Fractures, Bone/complications/diagnosis
Risk Factors
*Heart Injuries/etiology/diagnosis
*Myocardial Contusions/etiology/diagnosis

@article {pmid42660175,
year = {2026},
author = {Wyman, CG and Hirshfield, L},
title = {ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9f95},
pmid = {42660175},
issn = {1741-2552},
abstract = {OBJECTIVE: Interpretable brain-computer interface classifiers that generalize across subjects without calibration remain an open challenge. We evaluated whether prototype-based cross-attention can provide competitive, inherently interpretable event-related potential (ERP) classification across diverse paradigms under deployment-compatible conditions.

APPROACH: We propose ERP-XTTN (ERP Cross-Attention), a cross-attention architecture that routes input electroencephalographic peaks to fixed difference-wave prototypes via query-key-only cross-attention with no value projection. Classification is based directly on prototype similarity and a separate measure of component amplitude, so that the prototype content contributes to every decision by construction. Prototypes are derived automatically from prominent extrema in the training-fold grand-average difference wave. We evaluated across three public sources (BNCI Horizon 2020, HRI Cursor, and ERP CORE) encompassing eight ERP components (ERN, LRP, ErrP, N170, P300, N2pc, MMN, N400). Evaluations used leave-one-subject-out (LOSO) cross-validation with causal filtering at a three-channel montage, compared against EEGNet, EEG-Deformer, ERP Prototypical Matching Net (EPMN), and xDAWN with Riemannian geometry (xDAWN+RG).

MAIN RESULTS: At three channels, the mean performance gap between the best baseline and ERP-XTTN was 0.025 area under the receiver operating characteristic curve (AUROC). Prototype interventions confirmed that decisions depend on the physiological content of the prototypes rather than on the routing attention pattern alone. False positives morphologically resembled true positives more than true negatives did across all datasets, indicating classification errors are neurophysiologically explicable.

SIGNIFICANCE: ERP-XTTN generalizes across diverse ERP morphologies under causal, calibration-free conditions, while retaining competitive performance and decisions that depend directly on physiological prototype content at a three-channel montage. Unlike post-hoc explanation methods for black-box models, the basis of each decision is directly observable in the trained model itself. To our knowledge, this is the first epoch-level LOSO benchmark on ERP CORE.},
}

@article {pmid42652021,
year = {2026},
author = {Lukanov, S and Dyugmedzhiev, A and Slavchev, M and Zheleva, B},
title = {Effects of Size, Body Mass and Body Condition on Tonic Immobility Occurrence in Lissotriton vulgaris.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {16},
pages = {},
pmid = {42652021},
issn = {2076-2615},
support = {КP-06-N81/11//Bulgarian National Science Fund/ ; },
abstract = {Tonic immobility is a state of natural paralysis that species across the animal kingdom exhibit as an instinctive response to an external threat. In ectotherms, it has been linked with temperature, but the effects of individual size, body mass and body condition are still poorly understood. We hypothesize that under natural conditions, size, body mass and body condition play an important role in tonic immobility occurrence in smaller ectothermic animals such as the Smooth newt. In this study, we photographed, measured and weighed 357 adult Lissotriton vulgaris from four populations across Bulgaria in the period December 2025-June 2026. All observed cases of tonic immobility were noted and subsequently analyzed against the effects of newt size, body mass and body condition, as well as ambient air temperature. Our results indicated that smaller newts, with lower body mass, were more prone to displaying the behaviour than larger newts, while sex and ambient temperature had no observable effects.},
}

@article {pmid42653040,
year = {2026},
author = {Tomás, DJ and Pais-Vieira, M and Pais-Vieira, C},
title = {Linking Embodiment, Simulator Sickness, and EEG Activity During XR-BCI Use: A Single-Participant Case Study.},
journal = {Life (Basel, Switzerland)},
volume = {16},
number = {8},
pages = {},
pmid = {42653040},
issn = {2075-1729},
support = {FCT exploratory project 2023.11597.PEX//Fundação para a Ciência e Tecnologia/ ; UIDB/04501/2025//Fundação para a Ciência e Tecnologia/ ; UIDB/04279/2025//Fundação para a Ciência e Tecnologia/ ; },
abstract = {Background: Subjective experience is increasingly recognised as an important component of brain-computer interface (BCI) performance in extended reality (XR) environments. Although embodiment and simulator sickness are known to influence user experience, their relationships with cortical activity during XR-BCI operation remain poorly understood. Building upon our previous investigations of embodiment and simulator sickness in XR-BCIs, the present study examined whether these subjective dimensions are associated with distinct neurophysiological patterns during repeated XR-BCI use in a participant with chronic spinal cord injury (SCI). Methods: Seventeen XR-BCI sessions performed by a participant with chronic complete SCI were analysed. Bayesian correlation analyses examined associations among embodiment, simulator sickness, BCI performance, and EEG activity. Multiple linear regression was used to identify variables independently associated with sensorimotor beta activity, and the robustness of the regression findings was evaluated using bootstrap estimation and leave-one-out sensitivity analyses. Results: Bayesian analyses identified two principal patterns of association. Sense of embodiment was positively associated with frontal theta activity (F3), whereas simulator sickness showed a negative association with sensorimotor beta activity (C3-C4). As expected, classifier acquisition accuracy was strongly associated with subsequent BCI performance. Multiple regression demonstrated that simulator sickness was the only variable independently associated with C3-C4 beta activity after accounting for embodiment and BCI performance. This association remained robust following bootstrap estimation and leave-one-out sensitivity analyses. Conclusions: Although limited to a single participant, these findings suggest that different dimensions of subjective experience during XR-BCI operation are associated with partially distinct neurophysiological correlates. In particular, simulator sickness was the variable most consistently associated with sensorimotor beta activity across all analyses. These findings provide a foundation for future longitudinal investigations of the neural mechanisms linking subjective experience and cortical dynamics during XR-BCI use.},
}

@article {pmid42655181,
year = {2026},
author = {Kim, JW and Yeh, JY},
title = {Climatic Associations of Akabane Virus Occurrence in East Asia: Temperature-Driven Patterns Based on the Köppen-Geiger Classification.},
journal = {Microorganisms},
volume = {14},
number = {8},
pages = {},
pmid = {42655181},
issn = {2076-2607},
support = {RS-2025-02304897//Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries/ ; },
abstract = {Akabane virus (AKAV) is a Culicoides-borne arbovirus that causes congenital malformations and reproductive losses in ruminants, resulting in substantial economic losses in livestock production. Because vector activity and virus transmission are strongly influenced by environmental conditions, defining climatic factors associated with AKAV distribution is critical for understanding its epidemiology. However, such relationships have not been systematically evaluated in East Asia. In this study, we applied the Köppen-Geiger climate classification to characterize regional climatic zones in South Korea and Japan and examined their associations with AKAV case counts. AKAV cases were predominantly observed in temperate climate zones (Cfa and Cwa). Temperature-related variables showed consistent positive associations with AKAV case counts, with a 1 °C increase in annual mean temperature associated with approximately 1.38-1.56-fold increases in reported cases. In contrast, precipitation variables exhibited weak or negative associations. These findings indicate that temperature is an important climatic factor associated with AKAV case counts, suggesting that climate-dependent modulation of vector dynamics may contribute to observed patterns of AKAV occurrence. This study provides a climate-based framework for understanding the spatial distribution of AKAV and supports the development of targeted surveillance and control strategies under changing environmental conditions.},
}

@article {pmid42655407,
year = {2026},
author = {Abbasi, MA and Abbasi, HF and Arsalan, M and Khan, D and Annuk, A and Yu, X},
title = {A Comprehensive Review of Sequence and Generative Models in Motor Imagery (MI) Classification for Brain-Computer Interfaces (BCIs).},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {16},
pages = {},
pmid = {42655407},
issn = {1424-8220},
support = {TARISTU24-TK12//Estonian Research Council/ ; },
mesh = {*Brain-Computer Interfaces ; Humans ; Generative Adversarial Networks ; Autoencoder ; Recurrent Neural Networks ; Signal Processing, Computer-Assisted ; Electroencephalography ; Neural Networks, Computer ; Generative Artificial Intelligence ; Deep Learning ; Brain/physiology ; *Imagination/physiology ; },
abstract = {Motor imagery (MI) classification serves as the backbone to brain-computer interfaces (BCIs) by strengthening the communication bridge between the human brain and external peripheral devices. The past two decades have witnessed unprecedented success in MI-BCIs, with applications not only in medical fields but also in several other domains, such as gaming and robotic control. Initially, MI classification primarily relied on classical signal processing techniques that were heavily impacted by signal variations; however, recent trends in deep learning (DL), specifically in sequence-oriented, attention-based, hybrid, and generative architectures such as recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers have significantly improved the efficiency and robustness of MI classification. This study presents a comprehensive review of these sequence-oriented, attention-based, hybrid, and generative architectures including RNNs, VAEs, GANs, and transformers, comparing their robustness across various public MI datasets, highlighting their challenges, such as inter-subject variation, low signal-to-noise ratio (SNR), and the obstacles in real-time signal classification. We perform an in-depth analysis on the strengths and limitations of traditional models such as RNNs and LSTMs as well as emergent models such as VAEs and transformers, which have demonstrated superior performance in extracting the intricate patterns of the EEG data with low latency. Moreover, we critically examine the future potential of such models in overcoming current bottlenecks, such as weak generalization on unseen data and high computational load. This study aims to assist researchers in attaining significant insights into the state-of-the-art sequence, attention-based, hybrid, and generative models used in MI classification, thus offering a direction for future innovation.},
}

*Brain-Computer Interfaces
Humans
Generative Adversarial Networks
Autoencoder
Recurrent Neural Networks
Signal Processing, Computer-Assisted
Electroencephalography
Neural Networks, Computer
Generative Artificial Intelligence
Deep Learning
Brain/physiology
*Imagination/physiology

@article {pmid42655412,
year = {2026},
author = {Angrisani, L and De Benedetto, E and De Maria, A and Duraccio, L and Tedesco, A},
title = {Adoption of Deep Learning Methods for SSVEP Classification in XR-Based Wearable Brain-Computer Interfaces.},
journal = {Sensors (Basel, Switzerland)},
volume = {26},
number = {16},
pages = {},
pmid = {42655412},
issn = {1424-8220},
mesh = {*Brain-Computer Interfaces ; Humans ; *Evoked Potentials, Visual/physiology ; *Deep Learning ; *Wearable Electronic Devices ; Electroencephalography ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {This paper addresses steady-state visually evoked potential (SSVEP) classification in wearable extended-reality (XR) brain-computer interfaces (BCIs), with a threefold objective. First, it investigates the effectiveness of deep learning (DL)-based SSVEP classification under XR stimulation, where platform-dependent rendering, optical see-through visualization, reduced luminance contrast, and interaction with the real environment may degrade the quality of the elicited EEG response. Second, a metrology-based performance assessment is proposed according to the Guide to the Expression of Uncertainty in Measurement (GUM), with classification accuracy and information transfer rate (ITR) expressed as best estimates with associated standard uncertainties. Finally, EEG channel reduction is analyzed toward lightweight XR-SSVEP implementations. As a representative SSVEP-specific DL model, the SSVEP time-frequency fusion network (SSVEP-TFFNet) is evaluated on an open XR benchmark dataset comprising 30 subjects and 1200 trials acquired using Microsoft HoloLens 2. A subject-independent comparison with filter-bank canonical correlation analysis (FBCCA) is performed, while intra- and inter-subject variability are incorporated into the uncertainty evaluation. Results show that SSVEP-TFFNet outperforms FBCCA under the considered XR conditions. Moreover, reduced 6- and 4-channel configurations preserve performance close to the full 8-channel montage. These findings provide evidence of the potential of suitably selected DL models for XR-based SSVEP classification and support uncertainty-aware, reduced-electrode wearable implementations.},
}

*Brain-Computer Interfaces
Humans
*Evoked Potentials, Visual/physiology
*Deep Learning
*Wearable Electronic Devices
Electroencephalography
Algorithms
Signal Processing, Computer-Assisted

@article {pmid42656097,
year = {2026},
author = {Fu, Y and Yao, H and Li, T and Zhao, L and Yang, X and Luo, R and Xu, J},
title = {[Analysis of the constraints of inherent limitations on the capability boundaries of brain-computer interfaces and corresponding strategies].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {4},
pages = {669-677},
pmid = {42656097},
issn = {1001-5515},
mesh = {*Brain-Computer Interfaces ; Humans ; *Signal Processing, Computer-Assisted ; Electroencephalography ; Algorithms ; Signal-To-Noise Ratio ; *Brain/physiology ; },
abstract = {Brain-computer interfaces (BCIs) has developed rapidly in recent years, yet a systematic understanding of its inherent limitations and capability boundaries remains insufficient. This paper analyzes the constraint mechanisms through which inherent limitations shape BCI capability boundaries and discusses corresponding strategies from the perspectives of neural information generation, representation, acquisition, and utilization. The analysis indicates that the inherent limitations of BCI mainly arise from the dynamic nature of neural coding, inter-individual variability, low signal-to-noise ratios, partial observability, and paradigm dependence. As the intrinsic basis for capability boundary formation, these limitations jointly constrain the capability boundaries at the neural information, human-factors, and system levels, which are manifested as the upper performance limits of decoding accuracy, information transfer rate, complex intention decoding, user experience, as well as system stability, reliability, and safety. To address these constraints, this paper summarizes representative strategies, including information enhancement, adaptive decoding, human-machine collaboration, and system optimization. The analysis suggests that improvements in BCI performance fundamentally rely on enhancing neural information utilization and progressively expanding achievable capability boundaries under existing constraints, rather than overcoming their underlying limitations. The proposed framework provides a theoretical basis for understanding the relationship between inherent limitations and capability boundaries, and provides a reference for BCI research, technological innovation, practical applications, and scientific communication.},
}

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

@article {pmid42656100,
year = {2026},
author = {Lin, X and Zhang, S and Huang, S},
title = {[Dual-stream temporal-frequency convolutional network with additive attention for auditory attention decoding].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {4},
pages = {695-703},
pmid = {42656100},
issn = {1001-5515},
mesh = {Humans ; *Attention/physiology ; Convolutional Neural Networks ; *Electroencephalography ; *Signal Processing, Computer-Assisted ; *Auditory Perception ; Algorithms ; },
abstract = {In complex auditory environments, humans can selectively attend to a target speaker while suppressing interfering sources. Electroencephalography (EEG)-based auditory attention decoding (AAD) aims to identify the speech source an individual is attending to, holding significant potential for applications in hearing aids and human-machine interaction. However, existing methods still exhibit notable limitations in modeling dynamic time-frequency features, extracting multi-scale representations, and computational efficiency.To address these challenges, we propose a dual-stream time-frequency convolutional network with additive attention. The temporal branch employs a temporal convolutional additive attention module to integrate temporal and channel attention, enhancing the modeling of key EEG time segments and feature channels. The spectral branch adopts a spatial-spectral feature extractor and a multi-scale feature enhancement module to improve sensitivity to and modeling of frequency variation patterns. The global features from both branches are ultimately fused for attention direction classification. Under a 1-second decision window, our model achieved accuracy rates of 96.5%, 86.3%, 73.1%, and 73.5% on the KUL, DTU, AVED (audio-only), and AVED (audio-visual) datasets, respectively. These results significantly outperform state-of-the-art methods and demonstrate the effectiveness and superiority of the proposed approach.},
}

Humans
*Attention/physiology
Convolutional Neural Networks
*Electroencephalography
*Signal Processing, Computer-Assisted
*Auditory Perception
Algorithms

@article {pmid42656117,
year = {2026},
author = {Ma, Y and Ding, P and Cheng, Y and Li, T and Zhao, L and Gong, A and Nan, W and Fu, Y and Xu, J},
title = {[Classification of systemic lupus erythematosus resting-state functional magnetic resonance imaging data based on the end-to-end model].},
journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi},
volume = {43},
number = {4},
pages = {845-852},
pmid = {42656117},
issn = {1001-5515},
mesh = {Humans ; *Lupus Erythematosus, Systemic/diagnostic imaging/physiopathology ; *Magnetic Resonance Imaging/methods ; *Brain/diagnostic imaging/physiopathology ; },
abstract = {Systemic lupus erythematosus (SLE) frequently involves the central nervous system, inducing abnormal alterations in brain functional and structural networks and resulting in cognitive and psychological dysfunction in patients. To identify abnormal brain functional network patterns associated with SLE, this study adopted a dynamic threshold strategy to detect key functional connections and construct sparse brain functional networks. A graph transformation network (GTNet) was utilized to model the optimized networks, capturing local topological features and global dependencies to effectively identify abnormal patterns of SLE-related brain functional networks. Experimental results showed that the proposed model achieved an average classification accuracy of (87.48 ± 6.77)% on the resting-state functional magnetic resonance imaging dataset consisting of 107 SLE patients and 107 healthy controls. Further analysis showed that the difference in small-world properties between the two groups was statistically significant (t = -2.96, P < 0.01). In conclusion, the model constructed in this study provides a new scheme for the auxiliary diagnosis of SLE, and its automated classification architecture has potential application value.},
}

Humans
*Lupus Erythematosus, Systemic/diagnostic imaging/physiopathology
*Magnetic Resonance Imaging/methods
*Brain/diagnostic imaging/physiopathology

@article {pmid42658409,
year = {2026},
author = {Won, YJ and Jo, JJ and Yu, SA and Shim, SJ and Kim, KB and Kim, JH and Choi, SH and Cho, S},
title = {Development of a customized three-dimensional bolus using transparent gel wax and its application in electron beam therapy.},
journal = {Physical and engineering sciences in medicine},
volume = {},
number = {},
pages = {},
pmid = {42658409},
issn = {2662-4737},
abstract = {This study aimed to develop a customized bolus using commercially available transparent gel wax (TGW) without requiring 3D printing technology and to evaluate its applicability in radiotherapy, including its physical and dosimetric characteristics. A dental alginate impression was taken from the curved surface of a mastectomy phantom to replicate the patient's skin contour. Based on this, a silicone mould was fabricated, into which TGW was poured to form the customized bolus. The fabricated bolus was evaluated for its physical properties (density, electron density, homogeneity) and bolus conformity index (BCI), treatment planning dosimetric analysis, and actual dose measurements using optically stimulated luminescent (OSL) dosimeters, in comparison with a conventional vinyl gel sheet bolus (SuperFlex). The TGW bolus demonstrated high transparency and excellent homogeneity (standard deviation of internal HU: ±5.1), and its BCI was 0.03, indicating a very high level of conformity to the virtual bolus. Under 6 MeV and 8 MeV electron beam conditions, the TGW bolus showed mean dose errors of - 0.2% ± 1.3% and 0.5% ± 1.2%, respectively, which were numerically comparable to those of the SuperFlex bolus (0.6% ± 1.8% and - 2.1% ± 1.4%, respectively), with both systems providing clinically acceptable dose accuracy. In particular, the TGW bolus showed superior conformity and reproducibility in regions with high surface curvature, effectively reducing dose loss caused by air gaps. TGW is a low-cost, transparent, and flexible material that enables rapid fabrication (within one day) of a customized bolus through a simple moulding process. Its superior physical stability and equivalent dosimetric performance compared to existing products suggest strong potential for clinical application in radiotherapy.},
}

@article {pmid42659607,
year = {2026},
author = {Nnaka, T and McMahan, T and Teplitskiy, N and Arcuri, S and Akpan, IN and Farih, N and Brown, E and Macklin, H and Ahn, C and James, J and Sharma, S},
title = {Advancing Nursing Cognitive Capacity Through Generative AI and Immersive VR as a Structural Intervention for Burnout and Administrative Burden.},
journal = {Nursing administration quarterly},
volume = {50},
number = {4},
pages = {195-202},
pmid = {42659607},
issn = {1550-5103},
mesh = {Humans ; Generative Artificial Intelligence ; *Burnout, Professional/psychology/prevention & control ; *Cognition ; *Artificial Intelligence/trends ; *Brain-Computer Interfaces/standards ; Workload/psychology/standards ; },
abstract = {Nurses practice in clinical environments shaped by escalating administrative demands, documentation burdens, and persistent cognitive overload, structural pressures that contribute significantly to burnout and workforce attrition. Generative artificial intelligence (AI) offers a new form of cognitive support that, when implemented responsibly, can reduce the administrative load constraining nursing practice. We examine the early use of an in-house generative AI health assistant designed to predraft documentation and streamline communication. Rather than replacing clinical judgment, we position AI as a structural intervention that expands nurses' cognitive capacity and restores time for direct care and therapeutic engagement. We also explore the integration of electroencephalography data captured through brain-computer interface (BCI) devices such as Galea and EMOTIV headsets. This approach enables real-time insights into clinicians' cognitive workload and emotional states by translating neurophysiological signals into actionable information. By identifying neural indicators associated with stress, anxiety, and fatigue, the system can prompt timely supports as strain emerges. To ensure feasibility within clinical workflows, these wireless BCI systems are deployed during structured documentation periods and simulation-based training sessions. Combining neurotechnology, machine learning, and generative AI, this approach converts neural signals into meaningful insights that support clinician well-being while preserving professional integrity through nursing-led governance and safeguards.},
}

Humans
Generative Artificial Intelligence
*Burnout, Professional/psychology/prevention & control
*Cognition
*Artificial Intelligence/trends
*Brain-Computer Interfaces/standards
Workload/psychology/standards

@article {pmid42645039,
year = {2026},
author = {Zhang, R and Zhong, M and Ma, C and Xiao, Z and Zhang, Y and Liu, C},
title = {GLSTNet: A Global-Local Spatial Relations and Temporal Dynamics Network for EEG-Based Emotion Recognition.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645039},
issn = {2079-6374},
support = {62171123//National Natural Science Foundation of China/ ; 1132932502//Jiangsu University of Science and Technology/ ; 1132932304//Jiangsu University of Science and Technology/ ; 2023YFC3603600//the National Key Research and Development Program of China/ ; },
mesh = {Humans ; *Electroencephalography ; *Emotions ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Electroencephalography (EEG)-based emotion recognition is an important biosensing technique for affective brain-computer interfaces (BCIs), mental-state assessment, and physiological monitoring. Existing methods often rely on a single spectral descriptor or regular two-dimensional brain maps, which makes it difficult to jointly model local spatial representations, global spatial relations, and temporal dynamics. This paper proposes GLSTNet, a global-local spatial relations and temporal dynamics network for EEG emotion recognition. EEG trials are divided into short windows, from which multi-band spectral features are extracted and arranged into compact spatial maps. The local spatial encoder (LSE) learns local spatial and spatial-spectral representations from these compact multi-band spatial maps. The global spatial-relation encoder (GSRE) models long-range spatial relations between non-adjacent electrodes using a Pearson correlation prior and a learnable residual adjacency matrix. After local and global representations are integrated through gated fusion, the temporal dynamics encoder (TDE) models consecutive EEG windows using a gated recurrent unit with temporal attention. Comprehensive validation is conducted on two public EEG emotion datasets, the Database for Emotion Analysis using Physiological Signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). In the subject-dependence setting, GLSTNet achieves 93.50 ± 3.22% accuracy for valence and 93.79 ± 3.64% accuracy for arousal on DEAP, and 92.48 ± 3.30% accuracy on SEED. In the subject-independence setting with target-subject calibration, GLSTNet obtains 75.61 ± 6.19% and 79.57 ± 5.99% accuracy for DEAP valence and arousal, respectively, and 88.22 ± 4.70% accuracy on SEED. These results indicate that integrating global-local spatial relations with temporal dynamics provides an effective representation strategy for EEG-based emotion recognition.},
}

Humans
*Electroencephalography
*Emotions
Brain-Computer Interfaces
Signal Processing, Computer-Assisted
Algorithms

@article {pmid42645052,
year = {2026},
author = {Huang, D and Zhang, X and Li, Y and Wu, J and Yue, Y},
title = {MSA-CNN: A Multi-Scale Attention Convolutional Neural Network for fNIRS-Based Emotion Recognition.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645052},
issn = {2079-6374},
mesh = {Spectroscopy, Near-Infrared ; Convolutional Neural Networks ; Humans ; *Emotions ; },
abstract = {Functional near-infrared spectroscopy (fNIRS) has attracted increasing attention in affective brain-computer interface research due to its non-invasive nature, portability, and robustness to motion artifacts. However, substantial inter-subject variability in neural responses remains a major challenge for subject-independent emotion recognition. To address this issue, this work presents an effective integration of multi-scale temporal convolution and dual-attention mechanisms for subject-independent fNIRS emotion recognition evaluated under the leave-one-subject-out protocol within a single dataset. The proposed framework employs multi-scale temporal convolutions to capture hemodynamic characteristics at different temporal resolutions and incorporates channel and temporal attention mechanisms to adaptively emphasize informative brain regions and critical temporal segments. Experiments were conducted on both a self-collected fNIRS emotion dataset and the publicly available ENTER dataset using the Leave-One-Subject-Out (LOSO) evaluation protocol. On the self-collected dataset, MSA-CNN achieved an accuracy of 65.06 ± 7.10% with an F1-score of 0.605. On the ENTER dataset, the proposed model obtained an accuracy of 68.91% and an F1-score of 0.621, outperforming conventional machine learning approaches and several representative deep learning baselines. Ablation studies further demonstrated the positive contributions of both the multi-scale convolutional structure and the dual-attention mechanism. Experimental results on both the self-collected and ENTER datasets demonstrate that the proposed MSA-CNN achieves competitive emotion recognition performance under the LOSO protocol. Class-wise evaluation using precision, recall, and the F1-score further provides a comprehensive assessment of the model's classification behavior. These results indicate the effectiveness of the proposed framework for cross-subject fNIRS-based emotion recognition under the current experimental settings. The results indicate that multi-scale temporal feature learning combined with attention mechanisms can effectively enhance fNIRS-based emotion recognition performance and provides a promising framework for within-dataset cross-subject evaluation in fNIRS-based emotion recognition. Future work will focus on expanding the subject population, conducting cross-dataset train-test evaluations, and incorporating multimodal neural signals to further improve robustness and generalization.},
}

Spectroscopy, Near-Infrared
Convolutional Neural Networks
Humans
*Emotions

@article {pmid42645055,
year = {2026},
author = {Kim, JS and Choi, SI and Hwang, HJ and Han, CH},
title = {Deep Learning for Ear-EEG-Based Brain-Computer Interface: A Systematic Comparison and Design Insights.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645055},
issn = {2079-6374},
support = {No. RS-2025-25419004//National Research Foundation of Korea/ ; No. RS-2024-00397674//National Research Foundation of Korea/ ; IITP-2026-RS-2023-00258971//Ministry of Science and ICT/ ; },
mesh = {*Brain-Computer Interfaces ; *Deep Learning ; *Electroencephalography ; Humans ; *Ear ; Neural Networks, Computer ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG) measured inside or around ears, called ear-EEG, provides a practical measurement modality for daily brain-computer interface (BCI) applications. However, reliable decoding of mental imagery remains challenging due to the limited number of channels, low signal-to-noise ratio (SNR), and substantial inter- and intra-subject variability inherent to ear-EEG. Addressing these constraints requires advanced decoding strategies specifically optimized for this signal domain. In this study, we retrospectively analyze the ear-EEG dataset of a previous study in which a real-time endogenous BCI was evaluated using conventional machine learning. Specifically, we present an offline benchmark of 23 deep neural network architectures originally developed for scalp-EEG, which were adapted to ear-EEG and evaluated under an identical validation framework. To the best of our knowledge, this is the first systematic comparison of this breadth for ear-EEG-based mental-task classification. Beyond conventional performance comparison, we identify the optimal architecture by jointly considering statistical significance and a performance-cost trade-off, incorporating classification accuracy, parameter count, and measured computational cost. Our results demonstrate that FBLightConvNet achieves the highest classification accuracy among all evaluated models and outperforms common spatial pattern-linear discriminant analysis (CSP-LDA), a widely adopted and robust conventional baseline, on all three recording days, with the difference reaching statistical significance on Days 2 and 3. Notably, many state-of-the-art scalp-EEG models fail to generalize effectively to ear-EEG, highlighting the importance of architecture selection in this domain. These findings identify the best-performing architecture in this setting and indicate which architectural characteristics support effective ear-EEG decoding. Ultimately, this study offers practical design insights and a reproducible benchmarking framework for developing lightweight and high-performance deep learning models, which we hope will support future efforts toward real-world ear-EEG-based BCI systems.},
}

*Brain-Computer Interfaces
*Deep Learning
*Electroencephalography
Humans
*Ear
Neural Networks, Computer
Signal-To-Noise Ratio

@article {pmid42645069,
year = {2026},
author = {Zhang, Y and Shao, Q and Yang, H and Ren, X and Peng, X},
title = {DMG-GCN: A Dynamic Microstate-Guided Graph Convolutional Network for EEG Cognitive Workload Decoding in Air Traffic Control.},
journal = {Biosensors},
volume = {16},
number = {8},
pages = {},
pmid = {42645069},
issn = {2079-6374},
support = {2024YFC3014400//National Ministry of Science and Technology/ ; U2233208//National Natural Science Foundation of China/ ; },
mesh = {*Electroencephalography ; Graph Neural Networks ; Humans ; Brain-Computer Interfaces ; *Cognition ; *Aviation ; Workload ; Algorithms ; },
abstract = {Complex inter-subject variability induces severe distribution shifts in the physiological features of electroencephalography (EEG) for air traffic controllers (ATCOs). These inter-subject shifts limit the generalization and interpretability of passive brain-computer interfaces (pBCIs) during cognitive workload decoding. To address this, a Dynamic Microstate-Guided Graph Convolutional Network (DMG-GCN) is proposed for robust cross-subject workload recognition. This approach utilizes a Dynamic Selective Kernel Temporal Convolutional Block (DSK-TCB) to adaptively extract multi-scale temporal-spectral dynamics, while concurrently constructing a time-evolving adjacency matrix via a Microstate-Guided Dynamic Graph Block (MG-DGB) to disentangle topological sub-networks. A spatiotemporal graph convolution module then aggregates these representations, and a temporal self-attention mechanism focuses on task-critical transition moments. Extensive experiments on simulated multi-level air traffic control tasks demonstrate that the proposed model achieves an overall average accuracy of 80.30% and an average F1-score of 78.63% in cross-subject evaluations, significantly outperforming state-of-the-art baselines. Moreover, an exploratory interpretability analysis suggests that the extracted topological sub-networks exhibit spatial patterns consistent with specific brain network reorganizations, which encompass the transition from global distributed monitoring to temporal multimodal integration and parietal-occipital parallel processing during workload regulation. The framework provides a robust and analytically transparent pBCI solution for adaptive automation in modern aviation.},
}

*Electroencephalography
Graph Neural Networks
Humans
Brain-Computer Interfaces
*Cognition
*Aviation
Workload
Algorithms

@article {pmid42648317,
year = {2026},
author = {Hu, J and Gao, Z and Hao, Y and Xie, S and Li, X and Cai, Q and An, J},
title = {MTGNet: A task-oriented and spectrally guided framework for EEG denoising.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9ef1},
pmid = {42648317},
issn = {1741-2552},
abstract = {Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs), but its microvolt-level signals are easily contaminated by electromyography (EMG), electrooculography (EOG), and mixed physiological artifacts. This study develops an EEG denoising framework that suppresses artifacts while preserving information used by downstream biomedical artificial intelligence (AI) tasks. Approach. We propose MTGNet, a task-oriented and spectrally guided EEG denoising framework. MTGNet combines Low-Rank Adaptation (LoRA)-based Task-Aware Consistency Regularization (TACR), a spectrally aware Guidance Network, and parallel Mamba-Transformer backbone. A pretrained 11.97M-parameter backbone learns to preserve intrinsic EEG characteristics from paired noisy-clean data, while 0.33M LoRA parameters enable task-specific adaptation without requiring paired clean EEG references. Main results. On EEGDenoiseNet, MTGNet reduces spectral relative root-mean-square error (S-RRMSE) by over 18.9%, 31.5%, and 14.0% for EMG, EOG, and hybrid artifacts, respectively (p<0.001). On a real-world fatigue EEG dataset, it improves classification accuracy by 6.20-6.69 percentage points compared with unprocessed inputs (p<0.05). Ablation, cross-classifier, and cross-dataset analyses validate the proposed components and support the transferability of MTGNet across the evaluated settings. Significance. The proposed framework provides a practical approach to task-aware EEG denoising, while future work should further validate its applicability across real-world EEG settings involving diverse tasks, artifact types, and acquisition conditions.},
}

@article {pmid42648341,
year = {2026},
author = {Sawyer, A and Welle, CG and French, J and Kelemen, J and Bashford, L and Kramer, DR and McMullen, D and Franklin, R and Angle, MR and Herrmann, D and Oxley, T and Dister, J and Chetty, N and Radcliffe, EM and Kowalczyk, J and Bose, R and Farooqui, J and Hochberg, LR},
title = {Brain-computer interface clinical trial design considerations and clinical outcome assessments in pivotal studies: a summary of the 11th BCI society meeting 2025 workshop.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9eee},
pmid = {42648341},
issn = {1741-2552},
abstract = {The Brain Computer Interface (BCI) Society Meeting 2025 held a workshop in collaboration with the Implantable BCI Collaborative Community (iBCI-CC) to discuss the selection, development and validation of clinical trial outcome assessments (COAs) for pivotal studies of BCIs. The iBCI-CC Clinical Study Endpoints Workgroup aims to build consensus on a COA framework that can meet the demands of emerging iBCI trials. Approach: The workshop brought together diverse stakeholders from the iBCI-CC community and beyond, including industry, academic and government institutions to engage in pre-competitive collaborative discussion. Main results: Through presentations, panel discussions and breakout groups, workshop participants highlighted meaningful aspects of health (MAHs) relevant to iBCI users, clarified the need to define corresponding concepts of interest (COIs) and to identify or adapt clinical outcome assessments (COAs) capable of capturing both functional impact and real-world use. Key challenges highlighted during the workshop are patient heterogeneity, the lack of widely validated and fit-for-purpose COAs, and the need to capture meaningful outcomes in home and daily-life environments. Lessons drawn from trials such as ADAPT-PD underscore the value of capturing device performance in home and daily-life settings, highlighting the need for context-sensitive and flexible metrics that reflect patient priorities. Significance: This workshop lays a foundation for the iBCI-CC Clinical Study Endpoints Workgroup to establish a transparent process for identifying MAHs and COIs that are suitable for specific iBCI technologies, patient-informed, and conducive to regulatory approval and reimbursement. By combining rigorous, quantitative assessment with patient-informed goals, iBCI clinical trials can advance toward safe, effective, and accessible neurotechnologies that enhance how a person with a severe motor impairment feels, functions and survives. .},
}

@article {pmid42649661,
year = {2026},
author = {Yan, C and Zhao, Y and Zhou, W and Liu, G},
title = {A Learnable Entropy-Power Scaling Transform with a Radial Basis Function Network for Electroencephalography-Based Familiar and Unfamiliar Face Classification.},
journal = {Entropy (Basel, Switzerland)},
volume = {28},
number = {8},
pages = {},
pmid = {42649661},
issn = {1099-4300},
support = {No. 62401342//National Natural Science Foundation of China/ ; No. ZR2024QF092, ZR2024LZH007, and ZR2025ZD24//Natural Science Foundation of Shandong Province/ ; No. 2025A1515011826 and 2026A1515010768//Guangdong Basic and Applied Basic Research Foundation/ ; No. JCYJ20250604124702003//Shenzhen Fundamental Research Program/ ; No. 2024YFC2418300 and 2024YFC2418303//National Key Research and Development Program of China/ ; No. SCCl2025YB02//Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education/ ; },
abstract = {Familiar and unfamiliar face recognition is an important cognitive process with potential applications in brain-computer interface (BCI) systems and neurocognitive assessment. Classification of familiar and unfamiliar faces based on electroencephalography (EEG) remains challenging because discriminative information is distributed across multiple frequency bands, temporal windows, and scalp channels. This study proposes LEPST-RBFNet, which combines a Learnable Entropy-Power Scaling Transform (LEPST) and a radial basis function (RBF) network for EEG-based familiar and unfamiliar face classification. The model first segments the EEG into multi-scale time-frequency segments and then extracts local standard-deviation features. After that, a learnable entropy-power-inspired scaling transformation is applied using the LEPST module to obtain adaptive local time-frequency EEG entropy features. The transformed features are classified by an RBF prototype module with learnable centers and an adaptive kernel-width parameter. Experiments using a five-fold leave-one-block-out validation protocol show that LEPST-RBFNet achieves a superior average classification accuracy of 73.60%. Ablation and visualization analyses further indicate that the proposed model provides a competitive and interpretable framework for EEG-based familiar and unfamiliar face recognition.},
}

@article {pmid42649737,
year = {2026},
author = {Wu, W and Tang, J and Wang, Q and Yang, J},
title = {Optimizing Transcutaneous Electrical Stimulation Based on Frequency-Dependent Tissue Modeling and Signal Analysis.},
journal = {Bioengineering (Basel, Switzerland)},
volume = {13},
number = {8},
pages = {},
doi = {10.3390/bioengineering13080847},
pmid = {42649737},
issn = {2306-5354},
support = {KJQN202302808, KJQN202402841//Chongqing Education Commission of China/ ; K23ZG3030043//Chongqing Municipal Education Science Planning Project/ ; ygz2021101//Chongqing Medical and Pharmaceutical College/ ; },
abstract = {Transcutaneous electrical stimulation (TES) is limited by cutaneous discomfort caused by unavoidable activation of superficial sensory nerves during current delivery to deep targets. While psychophysical studies have empirically identified waveforms that reduce skin sensation, existing computational models typically employ quasi-static approximations that neglect the pronounced dielectric dispersion of biological tissues, leaving the biophysical mechanisms poorly understood. We developed a finite-element model of the human forearm incorporating the frequency-dependent dielectric properties of skin, fat, muscle, bone, and nerve tissues (DC to 1 MHz), coupled with a linear time-invariant signal-processing framework based on the system transfer function H(f). The model quantitatively reproduced the waveform-dependent sensation trends reported by Hsu et al. We introduced a penetration ratio to quantify deep-to-superficial nerve activation and found that all time-varying waveforms exhibit lower penetration than direct current (DC), revealing a skin-effect-like behavior of electrical current in biological tissues. Moreover, deep and superficial nerve activations co-varied under waveform parameter changes, indicating that waveform optimization alone cannot improve depth selectivity. Electrode spatial configuration was shown to offer a complementary strategy: positioning the active electrode close to the target muscle nerve while avoiding superficial cutaneous nerves, combined with sufficient transverse spacing, enhances depth selectivity. This work bridges psychophysical observations with tissue electrophysiology and provides a computational tool for waveform and electrode optimization in TES.},
}

@article {pmid42651174,
year = {2026},
author = {Tsai, PC and Tang, KT and Akpan, A and Lakany, H},
title = {Feasibility of Gamified EEG Neurofeedback Combined with Adaptive Working-Memory Training in Older Adults: A Pilot Study.},
journal = {Brain sciences},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/brainsci16080865},
pmid = {42651174},
issn = {2076-3425},
support = {//University of Liverpool/ ; },
abstract = {BACKGROUND: Falls among older adults are linked to central nervous system deterioration and executive function deficits.

OBJECTIVES: This study evaluates the feasibility of a gamified EEG-based neurofeedback intervention combined with adaptive working-memory training, targeting neural processes associated with attentional inhibition and working memory in older adults, and characterises, as exploratory secondary outcomes, the oscillatory and functional measures that change over the training period.

METHODS: Twenty healthy older adults (65.0±3.3 years) completed a 24-session longitudinal training programme. The intervention combined real-time alpha-band neurofeedback (NF), targeting left-frontal alpha activity associated with inhibitory control, with an adaptive N-back task engaging theta-band working memory processes. Behavioural outcomes included the Colour Trail Making Test A and B (CTMT-A/B), the Berg Balance Scale short form (BBS-3P), and the four-item Dynamic Gait Index (DGI-4). EEG was recorded at the Early, Middle, and Later stages to characterise training-related neural and behavioural changes.

RESULTS: The programme proved highly deliverable: adherence was 100% across all 24 sessions, no session was terminated early, and no severe adverse events occurred. Participants acquired control of the trained signal, with left-frontal alpha power rising across training at the neurofeedback target site, and frontal theta increased bilaterally under adaptive working memory load, consistent with the reduced hemispheric asymmetry characteristic of this age group. Scores changed in the direction of improvement on all four behavioural measures; however, functional changes were small, and cognitive changes cannot be separated from repeated-testing effects. Of sixteen candidate EEG-behaviour associations, four showed moderate-to-large participant-level coefficients and were retained as candidate associations: right-frontal alpha with balance (r=-0.64) and with gait adaptability (r=-0.48), and bilateral frontal theta with processing speed (r=-0.53 and -0.51). Several associations that appeared strong when observations were pooled across stages did not survive participant-level modelling, indicating that repeated-measures methods are needed in this literature. All associations are exploratory, uncorrected for multiplicity, and reported with effect sizes and confidence intervals.

CONCLUSIONS: The gamified neurofeedback and working-memory training programme was feasible, well tolerated, and fully adhered to in a supervised experimental setting by community-dwelling older adults and was accompanied by measurable modulation of the targeted frontal rhythms. This study delivers a shortlist of candidate EEG features, with the effect-size estimates needed to power a confirmatory trial. Because the design is single-arm, both functional scales approached ceiling, and the same test forms were repeated at each stage, practice effects cannot be separated from intervention effects, and these associations are hypothesis-generating; a sham-controlled trial in a fall-prone population is the appropriate next step.},
}

@article {pmid42651178,
year = {2026},
author = {Xu, Y and Otsuka, S and Nakagawa, S},
title = {Diffusion Inverse Filtering: Enhancing Functional Connectivity-Based Pattern Recognition by Counteracting Spatial Smoothing.},
journal = {Brain sciences},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/brainsci16080869},
pmid = {42651178},
issn = {2076-3425},
support = {JP24K03260//Japan Society for the Promotion of Science/ ; JPMJSP2109//Japan Science and Technology Agency/ ; },
abstract = {Background/Objectives: This study proposes Diffusion Inverse Filtering (DIF), a spatially informed transformation designed to counteract spatial smoothing in functional-connectivity representations (i.e., functional networks) and thereby enhance their discriminative power for pattern recognition. Spatial smoothing in electroencephalography (EEG) signals and derived features, such as functional connectivity, is largely attributed to volume conduction. Functional connectivity has been increasingly used in brain-computer interface (BCI) studies; however, this spatial smoothing can introduce spurious connections and distort functional-connectivity patterns. Methods: DIF approximates spatial smoothing in functional connectivity, which is potentially associated with volume conduction, as a diffusion-like process and applies a regularized inverse operation to transform the observed functional networks into networks with enhanced discriminative representations. The effectiveness of DIF in enhancing the discriminative power of functional-connectivity representations in pattern recognition was evaluated using emotion recognition as the paradigm task, which is a critical component of BCI systems. Experimental results show that DIF generally improves emotion-recognition performance relative to the originally observed functional networks under electrode-sparsification conditions, with the most consistent improvements observed for Pearson correlation coefficient (PCC) estimation. Both signal-level processing, which operates on EEG signals before functional-connectivity estimation, and function-al-connectivity-level transformations, including graph signal processing (GSP)-based filtering applied after functional-connectivity estimation, were included as comparators. Under this framework, DIF is also considered a functional-connectivity-level transformation, but does not rely on a GSP framework. Results: The performance of DIF demonstrates the potential of functional-connectivity-level transformation as a complement or alternative to signal-level processing for enhancing connectivity-based pattern recognition. Overall, DIF improves classification performance and offers strong compatibility with modern functional-connectivity-based BCI pipelines.},
}

@article {pmid42651451,
year = {2026},
author = {Chen, Y and Geng, F and Gong, M and Zhou, H and Hu, Y},
title = {A Mouse-Tracking Paradigm for Value-Driven Attentional Capture.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/bs16081273},
pmid = {42651451},
issn = {2076-328X},
support = {81971245//National Natural Science Foundation of China/ ; 82471511//National Natural Science Foundation of China/ ; },
abstract = {Reward learning is fundamental to adaptive behavior and exhibits substantial individual variability linked to addiction and other psychiatric conditions. Traditional value-driven attentional capture (VDAC) paradigms rely mainly on reaction time (RT) and accuracy, but it remains unknown whether engaging motor control processes could enhance sensitivity for detecting individual differences in reward-induced attentional biases. We developed a novel mouse-tracking VDAC paradigm that engages motor control processes. Two independent samples (n1 = 45, n2 = 59) completed the task and a battery of reward-related psychological scales (reward sensitivity, impulsivity, self-control, digital addiction tendency). A new index, maximum deviation (MD) of mouse trajectories, quantified how reward history biases attention and motor response. The paradigm replicated the classic RT-based VDAC effect. Trajectory-based measures revealed bidirectional effects: high-value distractors increased attraction and reduced active avoidance. The relative angle modulated VDAC strength, with the largest effect at 120°. ΔMD (high-value vs. absent) showed significant positive correlations with reward sensitivity, impulsivity, and addiction tendency, and negative correlations with self-control in both samples, whereas ΔRT showed no significant correlations. The consistent pattern across two independent samples suggests that mouse-tracking provides a complementary, process-sensitive index for detecting individual differences in reward-driven attention. This paradigm may serve as a useful tool for investigating cognitive markers of vulnerability to reward-related disorders, including addiction.},
}

@article {pmid42651598,
year = {2026},
author = {Pan, X and Shi, Z and Guo, X and Liu, X and Su, Y},
title = {Developmental Patterns in Cooperative Contexts: How Children Integrate Social Strategy and Resource Endowment in Third-Party Responses.},
journal = {Behavioral sciences (Basel, Switzerland)},
volume = {16},
number = {8},
pages = {},
doi = {10.3390/bs16081422},
pmid = {42651598},
issn = {2076-328X},
support = {32371111//National Natural Science Foundation of China/ ; 32071075//National Natural Science Foundation of China/ ; },
abstract = {This cross-sectional study examined how children integrated social strategy and initial resource endowment when responding to others as third-party observers in a cooperative context. Using an age-appropriate collective dilemma game, 141 Chinese children observed fictional peers choosing among cooperation, self-reliance, and free-riding strategies under conditions of high or low resource endowment, yielding a 3 (strategy) × 2 (resource endowment) experimental design. Children responded to each target through punishment, social evaluation, and partner choice. Results showed a strong preference for high-resource individuals among younger children (ages 5-6), which diminished with age, suggesting a developmental shift away from reliance on external cues such as resource. Across all age groups, children consistently responded most negatively to free riders, regardless of resource endowment, indicating their moral sensitivity to exploitative intent and fairness violations. Importantly, self-reliant individuals with high resources were viewed more favorably than those with low resources, indicating that children adjust their responses based on social strategy and resource endowment. These findings suggest two key developmental patterns: (1) an age-related decline in resource-based preferences and (2) early-emerging ability that enables children to weigh contextual cues when responding to non-cooperative behaviors. The study highlights age-related patterns in children's third-party responses, reflecting their sensitivity to both social strategy and situational information in cooperative contexts.},
}

@article {pmid42651898,
year = {2026},
author = {Wu, P and Peng, Y and Zhu, J and He, Q and Wang, J and Niu, X and Wang, S and Wang, Z and Shi, L},
title = {Categorical Representation of Numerosity in the Pigeon Entopallium: Coding Format and Temporal Dynamics.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {16},
pages = {},
doi = {10.3390/ani16162494},
pmid = {42651898},
issn = {2076-2615},
support = {62206253//National Natural Science Foundation of China/ ; 2024M752934//China Postdoctoral Science Foundation/ ; 262102211044//Henan Science and Technology Department/ ; },
abstract = {Numerosity perception is an evolutionarily conserved ability observed across diverse taxa, from insects and fish to birds and primates. Unlike traditional visual categories, numerosity possesses an intrinsic metric structure, making it well suited for quantitatively investigating how categorical representations emerge along the visual hierarchy. While numerical representations are well characterized in high-level associative areas of non-human primates and avian species, neuronal processing of numerosity in upstream regions remains largely unexplored. To address this gap, we recorded single-unit activity in the pigeon entopallium during a delayed match-to-numerosity task. We found a subset of neurons that encoded numerosity independently of the non-numerical feature controlled in the corresponding recording session, exhibiting quasi-monotonic increasing (QMI) or decreasing (QMD) response profiles. Compared with QMI neurons, QMD neurons exhibited a markedly later coding window and stronger category discriminability. Moreover, in both neuronal populations, the normalized response functions and category discriminability were better described by compressed numerosity scales than by a linear scale, with the logarithmic scale showing a modest overall advantage. These findings help refine current theoretical frameworks for how numerosity information is extracted and represented along the avian visual processing hierarchy, providing a comparative basis for cross-species research.},
}

@article {pmid42651974,
year = {2026},
author = {Yang, L and Guo, X and Tao, A and Li, Z},
title = {Hippocampal Local Field Potentials Encode Continuous Flight Speed in Homing Pigeons via Complementary Gamma and Theta Signatures.},
journal = {Animals : an open access journal from MDPI},
volume = {16},
number = {16},
pages = {},
doi = {10.3390/ani16162569},
pmid = {42651974},
issn = {2076-2615},
abstract = {Although the role of the mammalian hippocampus in representing locomotor speed has been widely investigated, how the avian hippocampus represents continuous flight speed under free-flight conditions in the outdoor environment remains unclear. In this study, we used homing pigeons as a model system and synchronously recorded hippocampal formation (HF) local field potentials (LFPs), global positioning system (GPS) trajectories, and inertial measurement unit (IMU) data during natural homing flights. We aimed to determine whether and how the avian HF encodes flight speed. Flight-speed-related neural features were extracted from both frequency-domain and time-domain signals, including the 50-70 Hz power spectral density (PSD) ratio and theta-demodulated amplitude (DAmp). We then constructed models for discrete flight-speed state decoding and continuous flight-speed prediction. The results showed that the 50-70 Hz PSD ratio in the HF was significantly negatively correlated with flight speed, whereas DAmp was significantly positively correlated with flight speed. Both features exhibited consistent speed-related trends across different spatial release sites. Support vector machine (SVM)-based classification showed that PSD, DAmp, and their combined features could effectively decode four flight-speed states, including non-flight, low-speed, medium-speed, and high-speed states, with the combined features achieving the best performance. Further Gaussian process regression (GPR) analysis demonstrated that the combined features predicted continuous flight speed more accurately than either single feature. These findings provide evidence that the avian hippocampal formation encodes continuous flight speed during natural navigation through the complementary integration of frequency-domain and time-domain features, extending the known role of the avian hippocampal formation from static spatial mapping to dynamic self-motion representation.},
}

@article {pmid42638125,
year = {2026},
author = {Zhang, H and Xian, R and Zhang, Y and Liu, X and Yang, G and Chen, G},
title = {Brain-computer interface for upper limb functional recovery post-stroke: a meta-analysis of improvement in upper limb motor function and changes in neurophysiological markers.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {23},
number = {1},
pages = {},
pmid = {42638125},
issn = {1743-0003},
mesh = {Humans ; *Upper Extremity/physiopathology ; *Recovery of Function/physiology ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Stroke/physiopathology ; },
abstract = {BACKGROUND: Brain-Computer Interface (BCI) has emerged as a promising intervention, facilitating recovery of upper limb motor function, enhancing associated neurophysiological markers, and improving activities of daily living (ADL) performance, which is measured using the Modified Barthel Index (MBI). Nevertheless, most existing research has centered on individual outcome domains, and thus critical questions remain unanswered. These questions include heterogeneity in treatment efficacy across different post-stroke phases, in addition to the consistency of BCI's effects across specific functional assessment scales, such as the Fugl-Meyer Upper Extremity Scale (FMA-UE) and the Action Research Arm Test (ARAT)-and on neurophysiological markers.

OBJECTIVE: To perform a systematic review and meta-analysis of the effects of BCI-mediated rehabilitation versus conventional rehabilitation on upper limb motor function, functional activities, neurophysiological markers, and MBI performance in patients with stroke.

METHODS: Systematic searches were performed in 7 databases for this study: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wan Fang Data, and China Biology Medicine Database (CBM). Inclusion criteria were randomized controlled trials (RCTs) comparing BCI-mediated rehabilitation with conventional rehabilitation in patients with upper limb dysfunction following stroke. The primary outcome measure was FMA-UE scores. Secondary outcome measures included functional activity levels as assessed by ARAT, neurophysiological markers (e.g., motor evoked potentials, MEPs, electroencephalography, EEG indices), and MBI scores. The methodological quality of the included studies was assessed using the validated evidence-based Cochrane Collaboration's RoB 2.0. In the meta-analysis, if the results of the heterogeneity test were less than 25%, the fixed-effect model was employed; otherwise, the random-effects model was used. For each outcome measure, the mean difference (MD) or standardized mean difference (SMD), together with their corresponding 95% confidence intervals (95% CI), were calculated.

RESULTS: After screening against the inclusion and exclusion criteria, 16 eligible randomized controlled trials (RCTs) were ultimately included, encompassing a total of 1761 patients. Compared with conventional rehabilitation interventions, BCI rehabilitation interventions exert a certain effect on improving patients 'upper limb motor function (SMD = 0.49, 95%CI;0.26-0.73, p = 0.037).Additionally, significant improvements were observed in ARAT scores (SMD = 0.53, 95% CI: 0.14-0.91, p = 0.035),neurophysiological markers(SMD = 0.53, 95% CI;0.08-0.98, p < 0.05)and MBI scores(SMD = 0.85, 95% CI;0.55-1.14, p < 0.001).Subgroup analyses of BCI-mediated rehabilitation interventions for upper limb motor function recovery in stroke patients demonstrated certain subgroup-specific disparities in the magnitude of therapeutic effects across different subgroups. With respect to stroke staging, BCI-mediated rehabilitation interventions conferred superior efficacy for motor function recovery in patients with subacute stroke, a finding plausibly attributable to the temporal course of post-stroke neural remodeling. Subgroup analyses stratified by intervention dosage demonstrated that both 10-15 and 20-30 sessions of BCI-mediated rehabilitation interventions resulted in clinically meaningful improvements in patients' upper limb function. No statistically significant difference was detected between these two treatment regimens, yet the 20-30-session protocol was associated with a relatively larger effect size. Furthermore, subgroup analyses stratified by intervention modality demonstrated that no statistically significant differences emerged across the distinct intervention approaches, and this observation plausibly suggests the rehabilitative benefits of divergent BCI paradigms in patients are uniformly mediated through neural circuit remodeling.

CONCLUSION: BCI emerges as a promising therapeutic modality for stroke rehabilitation, delivering substantial, statistically significant improvements across multiple key outcomes, including FMA-UE, ARAT, neurophysiological markers, and MBI scores with consistent statistical significance observed across all aforementioned domains.},
}

Humans
*Upper Extremity/physiopathology
*Recovery of Function/physiology
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
Stroke/physiopathology

@article {pmid42640039,
year = {2026},
author = {Peng, W and Yang, Y and Wang, J and Ren, X and Wang, D},
title = {Application of non-invasive electroencephalogram-based brain-computer interfaces in post-stroke hand function rehabilitation.},
journal = {Topics in stroke rehabilitation},
volume = {},
number = {},
pages = {1-17},
doi = {10.1080/10749357.2026.2723408},
pmid = {42640039},
issn = {1945-5119},
abstract = {BACKGROUND: Post-stroke hand dysfunction severely limits patients' independence, and conventional rehabilitation often fails those without active movement. Noninvasive EEG-based brain-computer interface (BCI) technology addresses this by creating a closed-loop feedback system rooted in Hebbian learning principles. This system decodes rhythmic signals from the sensorimotor cortex during imagined hand movements in real-time. The decoded intention is then translated into commands to drive exoskeletons, functional electrical stimulation (FES), or virtual reality (VR) devices, thereby moving the affected limb. This process strengthens or remodels damaged neural pathways, promoting motor recovery.

METHODS: This article systematically outlines the neurophysiological basis of EEG-BCI and three major rehabilitation paradigms: motor imagery with physical feedback, motor imagery with virtual/multisensory feedback, and the steady-state visual evoked potential (SSVEP)-driven paradigm.

RESULTS: Studies confirm these approaches can improve upper limb function, showing significant potential. However, widespread clinical use faces challenges like low signal-to-noise ratios, significant individual variability, and "BCI blindness."

CONCLUSIONS: Future work should focus on improving decoding algorithms, developing more user-friendly devices, deepening mechanistic understanding, and establishing standardized clinical assessments. This review aims to offer valuable guidance for subsequent research.},
}

@article {pmid42640770,
year = {2026},
author = {Pham, HM and Pham, TM and Nguyen, TH and Nguyen, LST and Nguyen, VK and Nguyen, LHK and Vo, QTN and Ha, HTT and Quan, TT},
title = {Towards Balanced Bias-Variance With SincDualFormer: A Dual-Scale Sinc-Filterbank Transformer Model With SR-BandMix Augmentation for Motor Imagery BCI.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3727259},
pmid = {42640770},
issn = {2168-2208},
abstract = {Motor imagery electroencephalography (MI-EEG) decoding remains challenging because of its low signal-to-noise ratio, substantial inter-subject variability, and limited training data. This study proposes SincDualFormer, a dual-rhythm architecture that combines physiologically constrained Sinc filtering and unconstrained temporal convolution within parallel mu and beta-aligned branches. The two complementary representations are independently recalibrated by BandSE and projected through depthwise spatial convolutions before pointwise fusion. A multi-scale Inception-TCN and a lightweight Transformer encoder are then employed to model local temporal patterns and long-range dependencies, respectively. We further introduce SR-BandMix, a joint time-frequency augmentation strategy that integrates same-class Segmentation-Reconstruction with fine-grained spectral mixing over multiple sub-bands within the MI-related 8-30 Hz range. Experiments on two public benchmarks and one private dataset show that SincDualFormer achieves average accuracies of 82.14%, 87.93%, and 58.50% on BCI Competition IV-2a, BCI Competition IV-2b, and the HCMIU MI Hand-Binary dataset, respectively. It achieves the highest average accuracy among the evaluated methods on all three datasets under their corresponding evaluation protocols. Across representative backbone architectures, SR-BandMix generally improves or maintains competitive performance relative to Segmentation-Reconstruction, BandMix, and no augmentation. In particular, SincDualFormer consistently achieves its best performance with SR-BandMix on both public benchmarks. Ablation, bias-variance, and visualization analyses further demonstrate the complementary contributions of rhythm-constrained filtering, data-driven temporal modeling, and joint time-frequency augmentation to robust MI-EEG decoding.},
}

@article {pmid42640816,
year = {2026},
author = {Park, N and Shin, Y and Kang, J and Lee, YE and Lee, A},
title = {Wireless Electroencephalography in Research on Children With Developmental Disabilities: Scoping Review.},
journal = {Journal of medical Internet research},
volume = {28},
number = {},
pages = {e84707},
pmid = {42640816},
issn = {1438-8871},
mesh = {Humans ; Child ; *Electroencephalography/instrumentation/methods ; *Developmental Disabilities/physiopathology ; *Wireless Technology ; },
abstract = {BACKGROUND: Wireless electroencephalography (EEG) systems offer practical advantages over conventional wired devices in the assessment of children with developmental disabilities (DDs), including enhanced portability, reduced participant burden, and ease of use. However, how these systems have been applied across diverse DD populations, research purposes, and clinical contexts remains unclear.

OBJECTIVE: This scoping review aimed to map available evidence on wireless EEG applications in children with DDs, characterize device specifications by application purpose, identify neurobehavioral challenges and corresponding methodological solutions, and assess data quality-related reporting practices.

METHODS: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews), PRISMA-S (PRISMA Statement for Reporting Literature Searches in Systematic Reviews), and the population, concept, and context framework: population, children aged<19 years with DDs; concept, studies using wireless EEG devices for data collection; and context, all research and clinical settings. A systematic search was conducted across 7 databases (PubMed, Embase, IEEE Xplore, Web of Science, CINAHL, PsycINFO, and Scopus) from their inception through December 2025. Screening was performed independently by 4 reviewers. Data on study characteristics, device specifications, neurobehavioral recording challenges, and data quality reporting were extracted and synthesized descriptively, including cross-tabulation of devices by application purpose.

RESULTS: Of 594 identified records, 64 studies enrolling 3103 participants met the inclusion criteria. Studies were published between 2005 and 2025, with an increasing trend in both publications and sample sizes. Attention-deficit/hyperactivity disorder (38/64, 59.4%) and autism spectrum disorder (20/64, 31.3%) were the most frequently studied conditions. Primary application domains were biomarker-driven assessment and diagnosis (33/64, 51.6%), brain-computer interface (BCI) technology (18/64, 28.1%), intervention evaluation (8/64, 12.5%), and task or state monitoring (5/64, 7.8%). Across 65 study-device pairs, consumer-grade devices predominated (31/65, 47.7%), followed by research-use-only (19/65, 29.2%) and medical devices (15/65, 23.1%). Purpose-driven patterns emerged: BCI studies favored low-channel, dry-electrode, consumer-grade devices, whereas biomarker-driven and intervention studies used higher channel counts and greater signal fidelity. Recurring neurobehavioral challenges (eg, inattention, sensory hypersensitivity, and motor impairment) were addressed through rapid, low-preparation electrode setups, child-friendly device designs, and adapted recording protocols such as home-based or caregiver-mediated sessions. Data quality-related reporting was substantially incomplete: 85.9% (55/64) did not report validation against a wired EEG system, 79.7% (51/64) did not specify impedance thresholds, and 12.5% (8/64) described no artifact handling.

CONCLUSIONS: This scoping review is the first to comprehensively map wireless EEG research in children across a broad spectrum of DDs-integrating diagnosis, study context, and device characteristics-rather than focusing on a single condition or purpose. This review highlights critical gaps in data quality-related reporting that limit the interpretability and comparability of current findings. Future studies should prioritize rigorous validation within DD cohorts and the development of population-specific guidelines for device selection, signal quality assurance, and reporting transparency.},
}

Humans
Child
*Electroencephalography/instrumentation/methods
*Developmental Disabilities/physiopathology
*Wireless Technology

@article {pmid42641774,
year = {2026},
author = {Wu, X and Zhu, Y and Du, S and Chen, Q and Chen, F and Xu, X and Long, C and Ge, D and Lei, Y and Bai, Y and Liu, D and Chen, Y and Fan, S and Yang, H and Zhu, Z and Jiang, Z and Li, Q and Lu, J and Zhang, B},
title = {Altered Olfactory Adaptation of Primary Olfactory Cortex-Hippocampus-Parietal Lobe in Alzheimer's Disease Continuum: An Olfactory Task fMRI Study.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122183},
doi = {10.1016/j.neuroimage.2026.122183},
pmid = {42641774},
issn = {1095-9572},
abstract = {Olfactory adaptation, the progressive reduction of neural responses to repeated odor stimulation, is closely linked to cognitive function and is altered in Alzheimer's disease (AD). However, its evolution across biomarker-defined stages and relationship with plasma p-tau217 remain unclear. We studied 168 participants classified by plasma p-tau217: cognitively normal (p-tau217-NC, n = 37; p-tau217+NC, n = 8), subjective cognitive decline (p-tau217-SCD, n = 57; p-tau217+SCD, n = 16), and mild cognitive impairment (p-tau217-MCI, n = 40; p-tau217+MCI, n = 10). Odor-induced fMRI with four concentrations (0.032%, 0.1%, 0.32%, 1.0%) presented in a fixed ascending order, although concentration effects could not be fully separated from time-related factors and other confounders, to assess activation in the primary olfactory cortex (POC), hippocampus (HP), and parietal lobe (PL). Receiver operating characteristic (ROC) analyses were performed using logistic regression models. In NC groups, adaptation emerged at 0.1% and 0.32% odor conditions. In p-tau217-SCD, POC adaptation was delayed to 1.0% odor condition, HP was largely preserved, and PL was dysregulated. p-tau217+SCD and MCI groups showed delayed and dysregulated adaptation across all regions. Odor adptations were associated with plasma p-tau217 levels and olfactory memory (p_unc < 0.05, p_FDR > 0.05). Furthermore, plasma p-tau217 partially mediated the relationship between adaptation-related alterations and olfactory memory. ROC analyses indicated that olfactory adaptation may distinguished individuals across disease stages, require further confirmation in independent cohorts. These findings reveal that impaired olfactory adaptation may represent an early signature associated with AD continuum, particularly in SCD and p-tau217-positive stages.},
}

@article {pmid42642203,
year = {2026},
author = {Deng, Q and Wu, K and Xie, L and Zhang, L and Wang, H and Zheng, X},
title = {[Emerging role of brain-computer interface in orthopedic rehabilitation and motor function restoration].},
journal = {Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery},
volume = {40},
number = {8},
pages = {1314-1319},
doi = {10.7507/1002-1892.202602068},
pmid = {42642203},
issn = {1002-1892},
mesh = {*Brain-Computer Interfaces ; Humans ; Recovery of Function ; Neuronal Plasticity ; },
abstract = {OBJECTIVE: To review the research progress and application prospects of brain-computer interface (BCI) in orthopedic rehabilitation and motor function restoration.

METHODS: Through literature analysis, the neurophysiological mechanisms of BCI in improving central motor drive deficits, technical paradigms, and clinical evidence in quadriceps inhibition after anterior cruciate ligament reconstruction and other orthopedic conditions were summarized, and the challenges in clinical translation were discussed.

RESULTS: BCI decodes movement-related neural signals and establishes closed-loop feedback between central and peripheral systems, which can enhance corticospinal tract excitability, induce neuroplasticity, and alleviate postoperative muscle inhibition and dysfunction caused by insufficient central motor drive. Available evidence indicates that motor imagery-based BCI training can improve quadriceps voluntary activation rate and reduce muscle strength loss after surgery, showing potential intervention value in other orthopedic conditions.

CONCLUSION: BCI holds promise as an important adjunct to conventional orthopedic rehabilitation, particularly for patients with central motor drive deficits. However, its clinical translation still faces challenges including insufficient mechanistic evidence, high patient heterogeneity, and lack of standardized training protocols, which represent key directions for future research.},
}

*Brain-Computer Interfaces
Humans
Recovery of Function
Neuronal Plasticity

@article {pmid42642419,
year = {2026},
author = {Zheng, Y and Liang, J and Li, Z and Liu, Y and Chu, X and Zhang, S and Wang, W and Bao, J and Liu, B and Ma, C and Yin, G and Deng, J and Chi, W and Meng, L and Shi, Y},
title = {Cryo-EM structures of biopsy-derived TTR fibrils in hereditary transthyretin amyloidosis.},
journal = {Nature communications},
volume = {17},
number = {1},
pages = {},
pmid = {42642419},
issn = {2041-1723},
support = {2025YFC3409700, 2024YFC3406200//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 82371415, 82230031, 32350018//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2024SSYS0018//Science and Technology Department of Zhejiang Province/ ; 2023-PT310-01//Chinese Academy of Medical Sciences (CAMS)/ ; 2024IR12//Peking University (PKU)/ ; 2024A1515011296//Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province)/ ; },
mesh = {Humans ; *Prealbumin/genetics/metabolism/ultrastructure/chemistry ; *Cryoelectron Microscopy/methods ; *Amyloid/ultrastructure/metabolism/genetics/chemistry ; *Amyloid Neuropathies, Familial/genetics/pathology/metabolism ; Mutation ; Biopsy ; Muscle, Skeletal/pathology/metabolism/ultrastructure ; Female ; Male ; },
abstract = {Hereditary transthyretin amyloidosis (ATTRv) is a fatal autosomal dominant disease characterized by systemic deposition of transthyretin (TTR) amyloid fibrils, leading to progressive neuropathy and cardiomyopathy. More than 130 pathogenic mutations in the TTR gene have been identified, but their roles in TTR fibril formation and disease pathogenesis remain unclear. Here, using cryo-electron microscopy (cryo-EM), we present nineteen high-resolution TTR fibril structures (1.9-3.4 Å) from gastrocnemius muscle biopsies and vitreous humor of ten living ATTRv patients carrying nine distinct heterozygous mutations. Deep-learning-based analysis of cryo-EM densities enables semi-quantitative assessment of mutant or wild-type dominance within fibrils. These compositional profiles, combined with their structures, suggest an association between disease onset and the TTR species (wild-type or mutant) that primarily initiates amyloid formation. This biopsy-based workflow broadens access to patient tissue for amyloid structural studies, enabling systematic investigation of heterogeneous hereditary amyloidoses and the role of mutations in amyloid formation.},
}

Humans
*Prealbumin/genetics/metabolism/ultrastructure/chemistry
*Cryoelectron Microscopy/methods
*Amyloid/ultrastructure/metabolism/genetics/chemistry
*Amyloid Neuropathies, Familial/genetics/pathology/metabolism
Mutation
Biopsy
Muscle, Skeletal/pathology/metabolism/ultrastructure
Female
Male

@article {pmid42642612,
year = {2026},
author = {Griebsch, I and Shrestha, S and Gotay, CC and Gore, JL and Curtis, F and Bottomley, A},
title = {Patient-Reported Outcome Instruments in Non-Muscle-Invasive Bladder Cancer (NMIBC): A Systematic Review.},
journal = {Oncology and therapy},
volume = {},
number = {},
pages = {},
pmid = {42642612},
issn = {2366-1089},
abstract = {INTRODUCTION: Non-muscle-invasive bladder cancer (NMIBC) is a chronic and recurrent condition requiring repeated intravesical treatments and lifelong surveillance, imposing a substantial burden on patients. Patient-reported outcomes (PROs) are increasingly required by regulatory bodies and HTA bodies to capture treatment impact from the patient perspective, yet no consensus on the optimal PRO instruments for use in NMIBC trials exists. This systematic review evaluated PRO instruments used in NMIBC and appraised their psychometric evidence using a structured evidence-maturity framework informed by the consensus-based standards for the selection of health measurement instruments (COSMIN) methodology.

METHODS: Six electronic databases (MEDLINE, CENTRAL, Embase, Scopus, Web of Science, and APA PsycINFO) were searched from January 2000 to 31 December 2024. Studies reporting the development, validation, or application of PRO instruments in adult NMIBC populations were eligible. Eligible instruments were required to be multi-item measures with psychometric validation, evidence of interpretability, or documented use in clinical trials. No language restrictions were applied. Conference abstracts and grey literature without peer-reviewed full texts were excluded. Data were extracted using Covidence, synthesized by instrument, and mapped against an NMIBC conceptual model. Evidence maturity was classified as tier 1 (well-established evidence base), tier 2 (moderate evidence base or context-specific validation), or tier 3 (preliminary evidence or under active development), on the basis of prespecified criteria. The COSMIN risk of bias checklist was used to appraise the methodological quality of included measurement property studies. Single-item measures were excluded from the main synthesis but are discussed in the context of future use.

RESULTS: A total of 39 studies covering eight PRO instruments met the inclusion criteria. The European organization for research and treatment of cancer quality of life questionnaire-non-muscle-invasive bladder cancer 24-item module (EORTC QLQ-NMIBC24) and bladder cancer index (BCI) demonstrated strong validity, reliability, and responsiveness in NMIBC, covering urinary, sexual, and recurrence-related domains, and were classified as tier 1 (well-established evidence base). The European organisation for the research and treatment of cancer quality of life questionnaire core 30 (EORTC QLQ-C30), functional assessment of cancer therapy-general (FACT-G), and FACT-Bladder were classified as tier 2, having been validated across broader cancer populations but with limited NMIBC-specific evidence. The NMIBC-symptom index (SI), PRO-common terminology criteria for adverse events (CTCAE), and M.D. Anderson symptom inventory (MDASI) were classified as tier 3, as conceptually relevant instruments with promising early validation but insufficient NMIBC-specific psychometric evidence for regulatory-grade use. A new structured psychometric comparison facilitates direct cross-instrument comparison of internal consistency, test-retest reliability, construct validity, responsiveness, and minimally important difference estimates. Conceptual mapping revealed persistent gaps in the coverage of recurrence anxiety, cumulative treatment burden, and long-term survivorship domains. Single-item burden measures (FACT-GP5 and EORTC item library Q168) may serve as pragmatic complements to multi-item instruments in regulatory submissions.

CONCLUSION: On the basis of the available evidence, the EORTC QLQ-NMIBC24 and BCI currently provide the strongest psychometric foundation for use as primary PRO instruments in NMIBC clinical trials, though this recommendation should be interpreted in light of the methodological limitations noted above. Core measures (QLQ-C30 and FACT-G) remain valuable when used in combination with disease-specific modules. Persistent gaps in the measurement of recurrence anxiety and treatment burden highlight the need for complementary instruments and targeted single items to achieve fully regulatory-aligned PRO assessment in future NMIBC trials. TRIAL REGISTRATION: This systematic review was registered in International Prospective Register of Systematic Reviews (PROSPERO) [CRD420251076486].},
}

@article {pmid42643271,
year = {2026},
author = {He, Q and Huang, CB and Chen, J and Thompson, B},
title = {Editorial: Advances in perceptual learning: new directions, techniques, and applications.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1935934},
pmid = {42643271},
issn = {1662-4548},
}

@article {pmid42643301,
year = {2026},
author = {Chandra, A and Ramaprasad, A and Rangaswamy, M},
title = {Ethics of brain-computer interface in mental healthcare.},
journal = {Frontiers in human neuroscience},
volume = {20},
number = {},
pages = {1846283},
pmid = {42643301},
issn = {1662-5161},
abstract = {Invasive, semi-invasive, and non-invasive brain-computer interfaces (BCI) are playing an increasingly important role in mental healthcare. They play a central role in all the stages of neuro-behavioural interventions, both by the care providers and the caretakers of a patient. The novelty of the technology and its rapid development create complex ethical challenges that must be balanced against the potential gains in efficiency and effectiveness of using the technology in mental healthcare. The paper presents an Ontology of Ethics of Brain-Computer Interface in Mental Healthcare, as a roadmap to guide to balance the ethics, effectiveness, and efficiency of the use of the technology. Like a 'Google Map' the ontology presents all the pathways by which the three issues can affect neuro-behavioural interventions using BCI technologies by the variety of agents that deliver mental healthcare. Using the ontology one can determine (a) the desired ethical pathways and reinforce them, (b) the undesired/unethical pathways and redirect them, and (c) discover novel ethical pathways and explore them.},
}

@article {pmid42643628,
year = {2026},
author = {Zhang, Y and Qian, K and Coyle, D and Dangana, M and Metcalfe, B},
title = {Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review.},
journal = {Frontiers in neuroscience},
volume = {20},
number = {},
pages = {1914165},
pmid = {42643628},
issn = {1662-4548},
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been widely explored for detecting and monitoring mental health-related states, with many existing studies focusing on identification and classification. Such approaches are primarily observational and provide limited support for intervention. Closed-loop EEG-based BCIs address this limitation by incorporating real-time feedback to observe neural activity. A key paradigm within this context is neurofeedback, in which users learn to modulate their own brain activity, with the aim of supporting improvements in mental states and in psychological functioning. In this work, we conducted a review of closed-loop EEG-based BCI interventions for mental health published since 2021, guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) reporting principles. A structured search was conducted across three databases (Scopus, Web of Science, and PubMed), yielding 1,101 records, of which 25 studies met the inclusion criteria. Advancements and observations were summarized across four categories: application, paradigm design and feedback mechanisms, signal processing and machine learning methods, and performance metrics and outcomes. In addition, this review discusses considerations related to signal processing and machine learning (ML), user interface design, and regulatory mechanisms across BCI interventions. Finally, potential directions for future research are outlined, including multimodal BCIs, domain adaptation, the integration of generative AI for BCI-based therapeutic interventions, and home-based deployment. Overall, current findings support the technical feasibility and emerging therapeutic potential of closed-loop EEG-based neurofeedback and BCI interventions in mental health applications, but the evidence base remains preliminary, as many studies were small pilot or feasibility studies.},
}

@article {pmid42644107,
year = {2026},
author = {Li, D and Deng, S and Li, Y and Zhang, Z and Nan, J and Yu, C and Yue, L and Yang, C and Zhang, H},
title = {An ERP dataset for multi-information identity authentication.},
journal = {Data in brief},
volume = {68},
number = {},
pages = {113148},
pmid = {42644107},
issn = {2352-3409},
abstract = {Conventional biometric authentication methods, such as fingerprint recognition, facial recognition, and voiceprint recognition, are vulnerable to spoofing, coercion, and data leakage. Electroencephalography (EEG), owing to its resistance to replication and involuntary nature, has shown considerable potential in secure biometric authentication, particularly in brain-computer interface (BCI) systems based on rapid serial visual presentation (RSVP). However, authentication relying solely on facial information remains limited by the single-dimensional nature of the authentication cue, insufficient resistance to attacks, and limited robustness in practical applications. Therefore, this study constructs, for the first time, a BIDS-compliant RSVP-EEG dataset for multi-information identity authentication, incorporating three types of authentication factors: target faces, target names, and verification images. Data were collected from 32 healthy participants, each of whom completed two experimental sessions for all three authentication tasks with an interval of >24 h. Identity-related and identity-unrelated stimuli were presented at a frequency of 10 Hz to elicit prominent recognition-related neural responses, and EEG signals were recorded using an eight-channel portable EEG system covering frontal and occipital regions. The proposed experimental design integrates multiple types and hierarchical levels of identity-related information, providing a solid data foundation for ERP analysis, RSVP-based cognitive experimental research, and the development of multi-information identity authentication systems.},
}

@article {pmid42644538,
year = {2026},
author = {Hu, Z and Zhuang, J and Wang, J and Jin, X and Fang, L and Hu, J},
title = {Body roundness index, frailty, and prevalent self-reported stroke: An exploratory cross-sectional decomposition analysis.},
journal = {Annals of the Academy of Medicine, Singapore},
volume = {},
number = {},
pages = {},
doi = {10.47102/annals-acadmedsg.2025411},
pmid = {42644538},
issn = {2972-4066},
abstract = {INTRODUCTION: Prior NHANES studies have reported nonlinear associations of the body roundness index (BRI) with prevalent stroke and frailty. The authors updated these observations using NHANES 1999-2023 and examined the additional contribution of frailty to the BRI-prevalent self-reported stroke association.

METHOD: This cross-sectional study included 36,324 adults. Stroke was defined by self-reported physician diagnosis. Survey-weighted logistic regression and restricted cubic splines were used to assess BRI associations. Expanded models were fitted with and without the frailty index (FI). A post hoc 2-piecewise model estimated a potential BRI breakpoint using 1000 bootstrap resamples. Incremental discrimination was evaluated across nested age-sex, age-sex-BRI, and age-sex-BRI-FI models. Analyses were interpreted as cross-sectional associations.

RESULTS: In the primary adjusted model, higher BRI was associated with prevalent self-reported stroke (odds ratio [OR]=1.08, 95% confidence interval [CI] 1.05-1.12). The spline showed increasing odds across lower-to-middle BRI values, followed by attenuation at higher values. The exploratory breakpoint was 6.34 (bootstrap 95% CI 6.14-7.65). In expanded sensitivity analyses, the BRI estimate was attenuated without FI (OR=1.01, 95% CI 0.98-1.04) and became inverse after FI was included (OR=0.91, 95% CI 0.88-0.95). FI remained strongly associated with prevalent self-reported stroke (OR=2.38 per 0.1-unit increase, 95% CI 2.23-2.54). Adding BRI to age and sex increased area under the curve (AUC) from 0.763 to 0.769, while adding FI increased AUC to 0.868.

CONCLUSION: BRI showed a nonlinear, model-dependent association with prevalent self-reported stroke, and frailty provided additional cross-sectional information. These findings support prospective evaluation of BRI and frailty as complementary markers in stroke-related assessment.},
}

@article {pmid42635515,
year = {2026},
author = {Li, H and Dang, W and Liu, L and Du, P and Cui, X and Hao, Y and Hu, J and Han, K and Wang, X and Gao, Z},
title = {Graph convolution neural network channel selection with attention for motor imagery EEG decoding.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {36},
number = {8},
pages = {},
doi = {10.1063/5.0319599},
pmid = {42635515},
issn = {1089-7682},
mesh = {*Graph Neural Networks ; Humans ; *Electroencephalography/methods ; Brain-Computer Interfaces ; *Attention ; *Imagination/physiology ; Algorithms ; Signal Processing, Computer-Assisted ; Convolutional Neural Networks ; },
abstract = {Accurate decoding of motor imagery electroencephalography (MI-EEG) signals is critical for practical brain-computer interface (BCI) systems. However, conventional approaches typically rely on dense multi-channel recordings, which not only introduce data redundancy but may also incorporate noise, thereby hindering real-world deployment. To address this challenge, we propose a graph neural network-based co-optimization framework that simultaneously performs channel selection and MI classification. The framework comprises two core components: one is the Key Channel Locator (KCL), which models EEG electrodes as graph nodes and identifies a subject-specific, fixed-size subset of informative channels through a dual-perspective evaluation that integrates graph convolutional topology with self-attention-derived feature importance, and the other is the UniEEG-Net, which efficiently decodes MI tasks from the selected channels using multi-scale temporal convolutions, depthwise separable spatial projection, and a self-attention mechanism. We extensively validate the proposed method on three datasets, including BCI Competition IV 2a, High Gamma, and a newly collected dataset. Experimental results demonstrate that our approach achieves performance comparable to that obtained with all channels while using significantly fewer electrodes. Moreover, UniEEG-Net exhibits classification accuracy surpassing current state-of-the-art models. The entire system is thus well-suited for real-world BCI applications, particularly in neurorehabilitation.},
}

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

@article {pmid42635627,
year = {2026},
author = {Pitt, KM and Thiessen, A and Fowler, G and Butler, E},
title = {Engaging Children in Brain-Computer Interface (BCI) Development: Utilizing an Interactive iPad Tool to Capture Picture-Based P300-Based BCI Augmentative and Alternative Communication Design Preferences.},
journal = {American journal of speech-language pathology},
volume = {},
number = {},
pages = {1-13},
doi = {10.1044/2026_AJSLP-26-00032},
pmid = {42635627},
issn = {1558-9110},
abstract = {INTRODUCTION: P300-based brain-computer interface augmentative and alternative communication (P300-BCI-AAC) systems show promise for supporting communication for children with severe speech and physical impairments. However, little is known about how children prefer these displays to be designed. This study examined children's design choices and the rationales guiding those choices to inform the development of user-centered, pediatric BCI-AAC displays.

METHOD: Thirty-eight typically developing children (8-12 years of age) used a P300-BCI-AAC design application to create their preferred picture-based interface. Participants selected features such as color, animation, and overlays. Semistructured interviews followed to capture reasons for their decisions.

RESULTS: Two themes guided design decisions: (a) supporting visual distinction to make items more recognizable and (b) personal factors influencing design choices. Most children selected preferred colors; animation effects, especially zooming animations; and sound cues. Animation was frequently chosen to help with visual accessibility and ease of target location and to support general personalization, whereas changes in background colors were commonly used to support the incorporation of preferred colors, and video GIFs and picture overlays commonly supported the incorporation of personal relevance. Some participants emphasized symbol visibility or reduced intensity.

CONCLUSIONS: Findings underscore the preliminary importance of motion, color, personalization, and visual clarity in pediatric BCI-AAC interfaces. These insights provide a foundation for developing customizable, engaging P300-BCI-AAC systems and highlight the need for research involving children who use AAC in daily life. Research incorporating individuals with motor difficulties is warranted to refine and extend conclusions.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.33289134.},
}

@article {pmid42636119,
year = {2026},
author = {Yang, Y and Sun, C and Lyu, R and Wang, Z and Chen, X and Lin, CT and Jung, TP and Wan, F},
title = {Adaptive Class-wise Multicentric Prototype Source-Free Domain Adaptation for Privacy-Preserving BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3726669},
pmid = {42636119},
issn = {2168-2208},
abstract = {Recently, conventional domain adaptation (DA) methods have demonstrated promising performance in cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, these methods require direct access to labeled source subject data, potentially compromising biometric privacy. Source-free domain adaptation (SFDA) addresses this issue by leveraging pre-trained source models with prototype-based pseudo-labeling, thereby eliminating the need for source data access. Nevertheless, current SFDA methods rely on oversimplified representations that fail to adequately capture EEG dynamics, resulting in two critical drawbacks: (1) oversimplified representations, using single centroids, fail to adequately capture complex neural manifolds, leading to intra-class collapse; (2) inadequate feature discrimination leads to error propagation in pseudo-labeling. To overcome these challenges, we propose the Adaptive Class-wise Multicentric Prototype-based SFDA (ACMP-SFDA) framework, which improves performance in new subjects while safeguarding personal privacy. Specifically, ACMP-SFDA dynamically constructs multiple prototypes per class to capture non-stationary EEG dynamics. Besides, we integrate semantic contrastive learning to improve inter-class discriminability while preserving the intrinsic structure of intra-class neural manifolds. Extensive experiments conducted across two BCI paradigms (motor imagery and affective BCI) demonstrate that ACMP-SFDA outperforms state-of-the-art methods, achieving 1.36$\%$, 1.96$\%$, and 1.94$\%$ accuracy improvements on MI2014001, MI2015001, and SEED, respectively, in cross-subject tasks.},
}

@article {pmid42636120,
year = {2026},
author = {Liu, H and Liu, G and Zhang, Y and Shi, Y and Zhang, Z and Zhang, D},
title = {TASTE: Trait-like and State-like Two-stream Framework for EEG Imputation.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3726783},
pmid = {42636120},
issn = {2168-2208},
abstract = {Electroencephalography (EEG) is a core sensing modality for brain-computer interfaces, yet real-world recordings often contain missing values due to motion artifacts, electrode issues, and device or channel failures. Unhandled missingness can bias analysis and degrade downstream decoding. Existing deep imputation methods typically formulate EEG recovery as a generic spatiotemporal completion problem and thus underutilize EEG-specific priors. Motivated by neurophysiological evidence, we characterize EEG signals with complementary trait-like regularities (reproducible temporal/spatial patterns) and state-like variations (acquisition noise and informative nonstationary dynamics). Based on this view, we propose TASTE, a Trait-like And State-like Two-stream framework for EEG imputation. TASTE integrates (i) a Multi-View Trait Modeling module that learns persistent temporal, spatial, and spatio-temporal priors via a tri-branch learnable codebook with squeeze-and-excitation fusion, and (ii) a State-Aware Reconstruction module that performs noise-robust feature-space completion and preserves meaningful nonstationarity using de-stationary attention. We evaluate TASTE on five public EEG benchmarks spanning diverse tasks. TASTE consistently improves imputation fidelity and downstream classification, achieving the best performance in most settings and delivering average MAE/MSE gains of 22.22%/28.60% over strong baselines. Source code is available at https://github.com/XJTU-EEG/TASTE.},
}

@article {pmid42637065,
year = {2026},
author = {Zhang, H and Chen, G and Wang, X and Wang, P and Guo, R and Ji, S and He, J and Li, M and He, F and Zhang, Y and Jian, X and Xu, M and Ming, D},
title = {Transcranial Acoustoelectric Brain Imaging with High Spatiotemporal Resolution.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {122179},
doi = {10.1016/j.neuroimage.2026.122179},
pmid = {42637065},
issn = {1095-9572},
abstract = {Non-invasive neuroimaging has long faced the inherent trade-off between spatial and temporal resolution. Acoustoelectric brain imaging (ABI) holds promise to bridge this gap by combining the spatial precision of focused ultrasound (millimeter-level) with the temporal resolution of electroencephalography (EEG) signals (millisecond-level). However, its transcranial application remains fundamentally challenged by skull-induced wavefront aberration and attenuation. Here, we developed a full ABI system featuring a custom 128-element ultrasound phased array. Our system integrates developed algorithms for transcranial phase and amplitude correction, which were experimentally validated through an ex vivo human skull. We demonstrate that our system enables precise intracranial focus steering (lateral error ≤ 0.2 mm), accurate source localization (error ≤ 0.8 mm), and high-fidelity waveform reconstruction (correlation coefficient > 0.84). This work addresses the fundamental challenge of skull-induced imaging quality degradation in ABI, providing algorithmic and systemic foundations for advancing non-invasive neuroimaging techniques.},
}

@article {pmid42637274,
year = {2026},
author = {Lima, TA and Malaquias, JB and Zanuncio, JC and Di Stefano, JG and Silva, CADD},
title = {A low-cost alternative to Parafilm for cotton boll weevil larval encapsulation and parasitism by Jaliscoa grandis (Hymenoptera: Pteromalidae).},
journal = {Journal of economic entomology},
volume = {},
number = {},
pages = {},
doi = {10.1093/jee/toag241},
pmid = {42637274},
issn = {1938-291X},
support = {001//Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/ ; 88887.838277/2023-00//Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/ ; 20.24.00.007.00.00//Better Cotton Initiative (BCI) for financial support of the "Embrapa Cotton"/ ; },
abstract = {Jaliscoa grandis (Burks) (Hymenoptera: Pteromalidae), one of the main parasitoids of the cotton boll weevil, Anthonomus grandis grandis Boheman (Coleoptera: Curculionidae), is mass-reared using larvae of this insect encapsulated in Parafilm, an efficient synthetic material; however, its high cost highlights the need to evaluate alternative films for this process. Three experiments were conducted. The first aimed to analyze the oviposition behavior of J. grandis in cotton squares with cotton boll weevil larvae and in cotton squares containing larvae encapsulated in different substrates. The second evaluated parasitism by J. grandis in larvae encapsulated in EVA cells with different thicknesses and types of paper covering them, compared to Parafilm. The third experiment analyzed parasitism using the alternative film and the production costs of this material for laboratory use. The numbers of perforations, eggs laid, progeny emergence, and mortality of cotton boll weevil larvae caused by the parasitoid J. grandis were similar in host larvae encapsulated with Parafilm or with EVA coated with tissue paper or paper towel. The operational and economic advantages of these alternative films, without reducing parasitism or the development of J. grandis progeny, reinforce their potential for use in augmentative biological control programs against the cotton boll weevil.},
}

@article {pmid42586149,
year = {2026},
author = {Nan, J and Yang, X and Jiang, H and Hong, C and Yang, J and Bai, Y and Wu, J and Ni, G},
title = {A multi-feature resting-state EEG framework for candidate EEG feature discovery in central vertigo.},
journal = {Journal of neural engineering},
volume = {23},
number = {4},
pages = {},
doi = {10.1088/1741-2552/ae98b9},
pmid = {42586149},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; Female ; *Vertigo/physiopathology/diagnosis/etiology ; Male ; Stroke/physiopathology/complications/diagnosis ; Middle Aged ; *Rest/physiology ; Aged ; Machine Learning ; },
abstract = {Objective. Central vertigo (CV) lacks objective electrophysiological measures for severity assessment and rehabilitation monitoring. We aimed to characterize multiscale resting-state EEG alterations and identify clinically interpretable candidate features in stroke-related CV.Approach. Resting-state EEG was analyzed in 50 patients with stroke-related CV (31 moderate, 19 severe) and 31 age-matched healthy controls. The framework integrated relative spectral power, cross-frequency coupling, PLV-based sensor-level phase synchrony, graph metrics, machine-learning feature ranking, and associations with balance confidence and dizziness severity.Main results. Severe CV showed widespread relative delta-power reductions of 28.7%-29.4% versus controls. Post hoc analysis showed lower global absolute delta power in severe CV than controls (Tukeyp= 0.0227; rank-based false-discovery-rate (FDR)q= 0.0459), although the absolute-power effect was less spatially extensive. Both patient groups showed reduced delta-theta and delta-beta amplitude-amplitude coupling (AAC), enhanced delta-alpha PPC, and theta-band increases in PLV-derived node degree, clustering, and global efficiency; local efficiency increased only in SV. Delta-beta AAC ranked highest across machine-learning methods and correlated moderately with balance confidence (ρ= 0.470,p= 0.001) and dizziness severity (ρ= - 0.472,p= 0.001). Zero-lag-robust measures showed the same theta ordering but were nonsignificant after FDR correction and did not establish volume-conduction-independent topology, supporting cautious PLV interpretation.Significance. Stroke-related CV involves coordinated alterations across oscillatory, cross-frequency, and sensor-level network measures. This interpretable framework identifies candidate EEG features for objective characterization that require external and longitudinal validation before clinical use.},
}

Humans
*Electroencephalography/methods
Female
*Vertigo/physiopathology/diagnosis/etiology
Male
Stroke/physiopathology/complications/diagnosis
Middle Aged
*Rest/physiology
Aged
Machine Learning

@article {pmid42627726,
year = {2026},
author = {Muñoz, JM},
title = {Neurotechnological Natives and the Neurotechnology Shift: A Research Agenda for the Brain-as-Interface Era.},
journal = {Annals of the New York Academy of Sciences},
volume = {1562},
number = {1},
pages = {e70381},
pmid = {42627726},
issn = {1749-6632},
mesh = {Humans ; *Brain/physiology ; *Neurosciences/trends/methods ; Cognition/physiology ; *Brain-Computer Interfaces/trends ; Research/trends ; },
abstract = {Neurotechnologies are increasingly moving beyond clinical and laboratory environments into a growing range of everyday contexts, including consumer markets, workplaces, and educational settings. This article presents the concept of the neurotechnology shift, understood as a technoscientific, cognitive, and sociocultural transformation through which neurotechnologies progressively expand into daily life, turning the brain itself into a shared interface between biological cognition and digital systems. This condition may eventually give rise to neurotechnological natives, a concept that is introduced here as a heuristic hypothesis intended to guide interdisciplinary research. Rather than predicting the emergence of a homogeneous generation shaped by neurotechnologies, the concept functions as an analytical tool to structure inquiry into the implications of brain-technology interaction in everyday contexts. To this end, the article outlines a research agenda organized across three dimensions of the neurotechnology shift: technoscientific infrastructures enabling scalable brain-technology interaction and neurodata ecosystems; cognitive implications related to neural plasticity, identity, impulse control, and language processes; and sociocultural transformations concerning governance, inequality and access, and collective imaginaries. Understanding the dynamics and bidirectional interactions across these three dimensions is essential for anticipating the ethical, legal, and societal implications of the emerging brain-as-interface era.},
}

Humans
*Brain/physiology
*Neurosciences/trends/methods
Cognition/physiology
*Brain-Computer Interfaces/trends
Research/trends

@article {pmid42627741,
year = {2026},
author = {Nair, K and Cecotti, H},
title = {Multiple-Classifier Binary Convolutional Siamese Networks for Code-Modulated Visual Evoked Potential Classification.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3726071},
pmid = {42627741},
issn = {1558-2531},
abstract = {OBJECTIVE: Non-invasive Brain-Computer Interfaces (BCIs) based on Code-Modulated Visual Evoked Potentials (c-VEPs) using electroencephalography (EEG) signals require robust classification algorithms. It is unclear whether the best approach is to use a similarity measure or to follow a discriminant method.

METHODS: We propose a multiple-classifier binary convolutional Siamese (MCBCS) network for single-trial c-VEP decoding, in which the multi-class recognition problem is decomposed into a set of class-specific binary similarity-learning tasks. The proposed MCBCS framework is systematically compared against a single multi-class Siamese network, convolutional neural networks for 63-bit m-sequence reconstruction and direct classification, and conventional correlation-based and canonical correlation analysis approaches. The study also investigates distance-based decoding strategies and the effect of temporal data augmentation with small to medium time shifts.

RESULTS: Experimental results on EEG data from 13 subjects demonstrate that the MCBCS architecture consistently outperforms other tested methods under within-subject evaluation, with a mean single-trial accuracy of 96.89%. However, the MCBCS approach achieves 96.17% under a leave-one-subject-out protocol, while EEGNet achieves 96.79%. Finally, the Wasserstein Distance (WD$_{1}$
) achieved the highest accuracy (93.88%) among the distance metrics.

CONCLUSION: The multiple-classifier convolutional binary Siamese network achieved the highest overall performance.

SIGNIFICANCE: The results highlight the effectiveness of class-specific similarity learning for robust compared to direct discriminant approaches.},
}

@article {pmid42627854,
year = {2026},
author = {Danayi, A and Soltanian-Zadeh, H},
title = {Improved motor imagery BCI performance via task-unaware compression in the BELT Bayesian Edge-Cloud architecture.},
journal = {PloS one},
volume = {21},
number = {8},
pages = {e0354976},
pmid = {42627854},
issn = {1932-6203},
mesh = {*Brain-Computer Interfaces ; Bayes Theorem ; Humans ; Data Compression/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Compression Algorithms ; Electroencephalography ; },
abstract = {Brain-computer interface (BCI) systems have advanced with deep learning, but they are still limited by designs tied to specific applications, poor scalability, weak portability, the need for user-specific adaptation, and privacy concerns. We present BELT, a modular Bayesian Edge-Cloud architecture based on three principles: (i) Bayesian priors and posteriors to balance generalization and subject-specific learning, (ii) lightweight classifiers suitable for embedded devices, and (iii) task-aware compression to reduce bandwidth and improve privacy in edge-cloud communication. To show feasibility, we implement BELT-lite as an instantiation of BELT, a lightweight version built only from linear time-invariant operations, making it directly compatible with digital signal processing hardware. Using the BCI Competition IV-2a and IV-2b motor imagery datasets (18 subjects total, ten-fold cross-validation), BELT-lite achieved strong posterior performance after subject-specific fine-tuning: mean accuracy of 87.9%±6.8% on Dataset B and 80.6%±8.6% on Dataset A with data augmentation. After adaptation, four subjects from Dataset B and two from Dataset A exceeded 90% accuracy. On ARM Cortex-A7 hardware, BELT-lite achieved a mean latency of 6.75 ms per sample, significantly faster than EEGNet's 8.36 ms (p < 10-17)-a 21% speed improvement-at the cost of a modest but statistically significant accuracy reduction of approximately 2.7 percentage points compared to EEGNet. Network Tuning Blocks allowed partial parameter freezing: classifier-only fine-tuning incurred a modest 2-5% accuracy drop while substantially reducing training cost. Compression via the task-unaware autoencoder reduced data size by 3.3× while maintaining high accuracy: prior-model performance stayed within ≈1% of the uncompressed baseline (with slight improvements in some configurations), full posterior fine-tuning showed a ≈1% drop, and classifier-only fine-tuning incurred a ≈3% drop-an acceptable trade-off for privacy-preserving edge-cloud communication, where only a compressed latent representation is transmitted instead of raw EEG. Notably, this task-unaware autoencoder (trained solely to reconstruct the input) consistently outperformed autoencoders that also incorporated classification objectives (task-aware or task-only), providing the best accuracy-compression trade-off across all fine-tuning scenarios. These findings show that BELT provides a principled design for modular and scalable BCIs, while BELT-lite demonstrates that the approach supports accurate, efficient, and portable implementations. Together, they point toward BCI systems that are more practical, mass-producible, and privacy-aware, enabling wider use in real-world settings.},
}

*Brain-Computer Interfaces
Bayes Theorem
Humans
Data Compression/methods
Algorithms
Signal Processing, Computer-Assisted
Compression Algorithms
Electroencephalography

@article {pmid42629376,
year = {2026},
author = {Priori, S and Ricci, P and Consoli, D and Micheli, A and Merlini, A and Andriulli, FP},
title = {Correction: A visual imagery paradigm for BCI strategies using imagined flickering patterns.},
journal = {Scientific reports},
volume = {16},
number = {1},
pages = {},
doi = {10.1038/s41598-026-67580-0},
pmid = {42629376},
issn = {2045-2322},
}

@article {pmid42631357,
year = {2026},
author = {Ciaglia, T and Carleo, G and Yang, Z and Matteo, FD and De Rosa, F and Smaldone, G and Gao, ZB and D'Alì, M and Turcio, R and Ma, D and Sarno, VD and Su, N and Giofrè, SV and Pepe, G and Campiglia, P and Bertamino, A and Miceli, F and Guo, J and Iraci, N and Ostacolo, C and Taglialatela, M},
title = {Molecular Basis for Activation to Inhibition Switching in Kv7.2 Channel Modulators.},
journal = {Angewandte Chemie (International ed. in English)},
volume = {},
number = {},
pages = {e7948330},
doi = {10.1002/anie.7948330},
pmid = {42631357},
issn = {1521-3773},
support = {CUP E63C22002170007//European Union-Next Generation EU, Mission 4, Component 2/ ; //Ministero della Salute/ ; //Regione Campania/ ; //European Rare Disease Research alliance/ ; 32371204//National Natural Science Foundation of China/ ; 32421001//National Natural Science Foundation of China/ ; 2025YFC3409700//National Key R&D Program of China/ ; //MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University/ ; //K.C. Wong Education Foundation/ ; },
abstract = {Kv7 voltage-gated potassium channels play a critical role in controlling electrical properties of excitable tissues. Neuronally-expressed Kv7 channels are involved in both common and rare neuropsychiatric disorders, ranging from epilepsy to depression and neurodegenerative diseases; thus, they represent attractive therapeutic targets. However, no Kv7 modulator is currently available for clinical use. Improved knowledge of the functional and structural determinants responsible for ligand-induced Kv7 channel modulation is likely to fill this gap. In the present work, we describe the cryo-electron microscopy structures of human Kv7.2 channels in complex with two retigabine analogues: compound 60 (c60), which we previously described as a Kv7 activator with improved pharmacokinetic and pharmacodynamic properties, and the newly designed compound 106 (c106), which acts as a potent Kv7 blocker. Although both compounds occupy the same pocket at the S5-S6 interface in the pore domain, docking and molecular dynamics simulations and electrophysiological experiments revealed that the opposite functional behavior is due to their differential interaction, involving the L307 residue. Thus, we herein provide novel mechanistic insights into the molecular mechanisms governing Kv7 channel modulation by exogenous ligands which may prove useful to target Kv7 channels with more potent and selective modulators.},
}

@article {pmid42631788,
year = {2026},
author = {Sivan, V and Alam, Z and Polavarapu, H and Manivel, S and Kumar, RP and O'Malley, GR and Ruzicka, F and Patel, NV},
title = {Brain-spine interface: an exploration of a potential future treatment for spinal cord injury.},
journal = {Neurosurgical review},
volume = {49},
number = {1},
pages = {},
pmid = {42631788},
issn = {1437-2320},
mesh = {Humans ; *Spinal Cord Injuries/therapy/surgery/physiopathology ; Animals ; Neuronal Plasticity/physiology ; Recovery of Function/physiology ; *Brain/surgery ; Brain-Computer Interfaces ; },
abstract = {Spinal cord injuries (SCIs) profoundly impact millions globally, leading to loss of motor and sensory functions below the injury site. Brain-spine interfaces (BSIs) represent an early-stage neuroprosthetic strategy that attempts to restore functional communication between cortical motor-intention signals and spinal sensorimotor circuits below the level of injury. Although early preclinical and highly selected clinical studies have shown encouraging motor outcomes, the evidence remains preliminary, and routine clinical use is limited by questions regarding safety, durability, patient selection, accessibility, and long-term functional benefit. BSI approaches are based on the observation that residual spinal pathways and sensorimotor circuits may remain partially responsive to neuromodulation even after injury. Along the way, technological advancements have significantly bolstered SCI treatment strategies, ranging from surgical interventions to regenerative therapies. Approaches such as neurostimulation and biomaterial-based strategies have shown potential in experimental and early translational settings, although their clinical efficacy and generalizability remain incompletely established. Furthermore, exploring neuroplasticity and the body's intrinsic ability to reorganize neural connections post-injury underscores the potential for spontaneous recovery in certain cases. However, integrating BSIs into clinical practice faces substantial hurdles, including technical challenges, ethical considerations, and the need for specialized training for healthcare providers. Despite these obstacles, BSIs and other novel treatments may have potential to improve the quality of life for SCI patients, although further clinical investigation is needed to establish their safety, efficacy, and generalizability. This review catalogs recent conceptual and technological developments contributing to the emergence of BSI.},
}

Humans
*Spinal Cord Injuries/therapy/surgery/physiopathology
Animals
Neuronal Plasticity/physiology
Recovery of Function/physiology
*Brain/surgery
Brain-Computer Interfaces

@article {pmid42625007,
year = {2026},
author = {Fares, H and Ronchini, M and Zamani, M and Farkhani, H and Chiappalone, M and Neftci, E and Moradi, F},
title = {Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.},
journal = {npj biomedical innovations},
volume = {3},
number = {1},
pages = {},
pmid = {42625007},
issn = {3005-1444},
support = {R402-2022-1413//Lundbeck Foundation/ ; No 767092//European Union's Horizon 2020 research and innovation program/ ; },
abstract = {Neurological disorders pose a growing global health burden, motivating advances in neural interfacing and artificial intelligence (AI). This review surveys state-of-the-art approaches for neural recording, stimulation and signal decoding and encoding across brain-computer interfaces, neuroprosthetics, and neuromodulation systems aimed at restoring function and treating neurological disorders. It highlights recent progress in AI, particularly neuromorphic computing and spiking neural networks (SNNs), and introduces Brain-Inspired Brain-Computer Interfaces (BI-BCIs) as a unifying framework for low-power, closed-loop, and miniaturized neuromorphic neurotechnologies.},
}

@article {pmid42625783,
year = {2026},
author = {Gu, W and Daly, I and He, X and Li, S and Lau, AT and Wang, X and Cichocki, A and Chen, Y and Jin, J},
title = {EFS-NET: EEG-fNIRS multi-scale fusion network based on spatial calibration.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {160},
pmid = {42625783},
issn = {1871-4080},
abstract = {Hybrid brain-computer interfaces (hBCIs) integrate multiple neuroimaging modalities and utilize their complementary information to address the inherent limitations of single-modality neural signal decoding. For electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) hybrid BCIs, advanced fusion algorithms are crucial to fully exploit the superior spatial localization capability of fNIRS and the millisecond-level temporal resolution of EEG. This work proposes an end-to-end spatial calibration-based multi-scale EEG-fNIRS fusion network named EFS-Net, which organically integrates EEG and fNIRS signals through a multi-scale spatio-temporal fusion architecture. The network consists of three complementary functional branches: a multi-scale temporal convolution branch for capturing rapidly changing cortical electrophysiological features of EEG, an EEG spatial branch for constructing latency-compensated cortical topographies to adapt to the delayed hemodynamic response of fNIRS, and a spatially calibrated fNIRS spatial branch for dynamically fusing spatial feature maps with EEG counterparts to generate temporally aligned and spatially enhanced neural representations. This three-branch fusion structure constructs abundant spatio-temporal feature embeddings and improves the discriminability of neural features. Evaluated on two public datasets including Word Generation (WG) and Mental Arithmetic (MA) with a rigorous subject-specific leave-one-session-out cross-validation protocol, EFS-Net achieves classification accuracies of 77.71 ± 8.23% and 81.69 ± 9.49% on the WG and MA datasets respectively, which surpasses state-of-the-art unimodal algorithms and traditional fusion models. Visualization results demonstrate that the designed alignment strategy can restore realistic cortical spatial distribution characteristics, providing a feasible solution for personalized neural signal decoding in hybrid BCIs.},
}

@article {pmid42626977,
year = {2026},
author = {Qin, Y and Li, M and Li, Y and Ma, M and Xu, J and Lu, Y and Shi, X and Cui, G and Zhao, H and Yang, K},
title = {Brain-computer interface training for motor recovery after stroke.},
journal = {The Cochrane database of systematic reviews},
volume = {8},
number = {8},
pages = {CD015065},
pmid = {42626977},
issn = {1469-493X},
mesh = {Humans ; Randomized Controlled Trials as Topic ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; *Recovery of Function ; Activities of Daily Living ; Stroke/complications ; Bias ; Adult ; Muscle Strength ; },
abstract = {RATIONALE: Stroke is one of the leading causes of death and disability worldwide. Motor dysfunction is a highly prevalent and disabling consequence of stroke. Brain-computer interface (BCI) training has emerged as a promising neurorehabilitative strategy that utilises closed-loop feedback to promote targeted neural plasticity and motor recovery. However, current clinical evidence remains fragmented.

OBJECTIVES: To assess the effects of brain-computer interface training for motor recovery in people after stroke.

SEARCH METHODS: We searched the Cochrane Stroke Group's Specialised Register, CENTRAL, MEDLINE, Embase, 11 other databases, trial registries, reference lists, and Google Scholar up to 27 October 2025, without language or time restrictions.

ELIGIBILITY CRITERIA: We included randomised controlled trials (RCTs) involving adults with stroke and motor dysfunction. We compared BCI training versus conventional rehabilitation, sham-BCI, or other active non-BCI interventions.

OUTCOMES: Critical outcomes were motor function (upper and lower extremity), activities of daily living (ADL), and adverse events. Important outcomes included measures of balance, muscle strength, spasticity, and neurological function.

RISK OF BIAS: Four review authors independently assessed risk of bias using the Cochrane risk of bias (RoB 1) tool.

SYNTHESIS METHODS: We pooled data using random-effects models, calculating risk ratios (RRs) and mean differences (MDs) or standardised mean differences (SMDs). We calculated 95% confidence intervals (CIs) using the Hartung-Knapp-Sidik-Jonkman (HKSJ) method. We calculated 95% prediction intervals when at least five studies were available. Heterogeneity was assessed using the I[2] statistic and evidence certainty was evaluated using the GRADE approach.

INCLUDED STUDIES: We included 43 RCTs involving a total of 1628 participants. The trials were conducted in 11 countries across hospital, rehabilitation unit, or outpatient-clinic settings. The trials primarily recruited participants with both ischaemic and haemorrhagic stroke, mostly in the subacute and chronic phases. The interventions predominantly utilised EEG-based motor imagery - BCI combined with robotic systems, functional electrical stimulation, or neurofeedback.

SYNTHESIS OF RESULTS: Most studies were at low risk of bias for incomplete outcome data and blinding of assessors, but at high or unclear risk for participant blinding and allocation concealment. Overall, the certainty of evidence was low to very low, downgraded primarily for these risk of bias concerns, severe imprecision (due to small sample sizes), and potential publication bias. BCI training versus conventional therapy BCI training may slightly improve upper extremity motor function (MD 4.67, 95% CI 2.25 to 7.09; 10 studies, 611 participants; low-certainty evidence). It is uncertain whether BCI training improves ADL due to very low-certainty evidence. It may result in little to no difference in lower extremity motor function (MD 2.62, 95% CI 2.17 to 3.07; 1 study, 64 participants; low-certainty evidence) and the risk of adverse events (RR 1.11, 95% CI 0.72 to 1.69; 7 studies, 624 participants; low-certainty evidence). BCI training may result in little to no difference in upper extremity spasticity compared to conventional therapy (MD -0.04, 95% CI -0.24 to 0.16; 1 study, 296 participants; low-certainty evidence). No studies reported information on balance, muscle strength, and neurological function for this comparison. BCI training versus active controls It is uncertain whether BCI training improves upper extremity motor function (SMD 0.58, 95% CI 0.23 to 0.92; 14 studies, 331 participants; very low-certainty evidence) and ADL (MD 8.52, 95% CI 1.76 to 15.29; 5 studies, 163 participants; very low-certainty evidence). It may result in little to no difference in lower extremity motor function (MD 2.46, 95% CI 0.71 to 4.21; 4 studies, 130 participants; low-certainty evidence). Furthermore, it is uncertain whether it affects adverse events (RR 0.72, 95% CI 0.23 to 2.32; 9 studies, 234 participants; very low-certainty evidence). BCI training may improve balance (MD 3.25, 95% CI 1.07 to 5.43; 6 studies, 165 participants; low-certainty evidence). It is uncertain whether it improves neurological function or muscle strength due to very low-certainty evidence. It may result in little to no difference in spasticity (low-certainty evidence). BCI training versus sham-BCI training The evidence is uncertain about the effect of BCI training on upper extremity motor function (SMD 0.22, 95% CI -0.08 to 0.52; 9 studies, 279 participants; low-certainty evidence), lower extremity motor function (MD 0.55, 95% CI -5.88 to 6.97; 3 studies, 106 participants; low-certainty evidence), and ADL (MD 6.64, 95% CI -9.95 to 23.23; 1 study, 28 participants; low-certainty evidence). It is uncertain whether BCI training increases the risk of adverse events compared to sham-BCI training (RR 1.35, 95% CI 0.32 to 5.72; 7 studies, 205 participants; very low-certainty evidence). BCI training may result in little to no difference in balance (MD 1.28, 95% CI -0.16 to 2.72; 1 study, 28 participants; low-certainty evidence). It is uncertain whether it improves muscle strength on the wrist extensor (MD 0.70, 95% CI 0.33 to 1.08; 2 studies, 51 participants), spasticity (SMD 0.30, 95% CI -1.00 to 1.61; 3 studies, 82 participants), and neurological function (MD 2.60, 95% CI -3.35 to 8.55; 1 study, 27 participants), all based on very low-certainty evidence.

AUTHORS' CONCLUSIONS: Compared with conventional therapy, BCI training may slightly improve upper extremity motor function, with little to no difference in lower extremity function. ADL effects are very uncertain. Compared with active controls, it may improve balance, with little to no difference in lower extremity function. Effects on upper extremity function and ADL are very uncertain. Compared with sham-BCI, BCI training may result in little to no difference in motor function or ADL. Regarding adverse events, it may result in little to no difference versus conventional therapy, but remains uncertain across other comparisons. The certainty of the evidence ranged from low to very low across all comparisons. These findings were primarily limited by high risk of bias, small sample sizes, heterogeneity, and possible publication bias. Future larger, adequately powered, and methodologically rigorous RCTs, particularly those utilising sham-BCI controls and standardised outcome measures, are needed to determine the specific therapeutic benefits of BCI training and to adequately assess potential harms.

FUNDING: This Cochrane Review had no dedicated funding.

REGISTRATION: Protocol (2022) DOI: 10.1002/14651858.CD015065.},
}

Humans
Randomized Controlled Trials as Topic
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
*Recovery of Function
Activities of Daily Living
Stroke/complications
Bias
Adult
Muscle Strength

@article {pmid42616624,
year = {2026},
author = {Li, S and Ouyang, J and Cui, Z and Wang, Z and Jia, T and Wan, F and Wu, D},
title = {Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2026.3725326},
pmid = {42616624},
issn = {2168-2208},
abstract = {Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints. While test-time adaptation (TTA) mitigates distribution shifts under online data streams without per-use calibration sessions, existing TTA approaches heavily rely on explicitly defined loss objectives that require backpropagation for updating model parameters, which incurs computational overhead, privacy risks, and sensitivity to noisy data streams. This paper proposes Backpropagation-Free Transformations (BFT), a TTA approach for EEG decoding that avoids these issues. BFT applies multiple sample-wise transformations, based on knowledge-guided augmentations or structured feature masking, to each test trial, producing multiple predictions for a single test sample using only forward passes. A learning-to-rank module, trained on source data, estimates the reliability of each transformed prediction, so that a weighted aggregation suppresses prediction uncertainty during online inference, with theoretical justification. Extensive experiments on five EEG datasets, covering motor imagery classification and driver drowsiness regression, demonstrate the effectiveness, versatility, robustness, and efficiency of BFT. This research enables lightweight plugand- play BCIs on resource-constrained devices, broadening the real-world deployment of EEG-based BCIs.},
}

@article {pmid42617634,
year = {2026},
author = {Li, Z and Wang, F and Lu, H and Luo, R and Li, TW and Zhao, L and Fu, Y},
title = {DRDNet: A dual-view representation decoupling network for handwriting imagery EEG classification.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ae9bef},
pmid = {42617634},
issn = {1741-2552},
abstract = {OBJECTIVE: Handwriting imagery (HI) based on electroencephalography (EEG) offers a non-invasive route to text input and complex intention expression for brain-computer interfaces (BCIs). However, HI EEG decoding is challenged by low signal-to-noise ratios, non-stationarity, cross-session distribution shifts, and the coexistence of continuous temporal trends and local high-response patterns.

APPROACH: We propose a Dual-View Representation Decoupling Network (DRDNet) for within-subject crosssession HI EEG classification. DRDNet first constructs two complementary temporal views from spatial EEG features using average and max pooling, corresponding to smooth trend-oriented and salient response-oriented representations. These views are then modeled by a bidirectional Mamba encoder and a Transformer encoder, respectively, and adaptively integrated through a time-step-level dynamic fusion mechanism followed by long short-term memory based temporal aggregation. The method is evaluated on two tasks from a public HI EEG dataset: Chinese character stroke handwriting imagery (CCSHI) and pinyin single-vowel handwriting imagery (SVHI).

MAIN RESULTS: DRDNet achieved average accuracies of 67.74% and 62.51%, with Cohen's kappa scores of 0.5968 and 0.5502, on CCSHI and SVHI, respectively. It outperformed seven representative EEG decoding baselines under the same crosssession protocol. Confusion matrices, feature visualization, ablation studies, structural variant analysis, and complexity evaluation further showed improved feature separability and a favorable balance between decoding performance and computational efficiency.

SIGNIFICANCE: The results indicate that decoupling HI EEG into complementary temporal views and matching them with heterogeneous temporal encoders provides an effective representation learning strategy for robust non-invasive handwriting BCI decoding.},
}

@article {pmid42619707,
year = {2026},
author = {Fogg, ZM and Card, NS and Wairagkar, M and Srinivasan, A and Singer-Clark, T and Hou, X and Okorokova, E and Peracha, H and Iacobacci, C and Brailow, T and Jude, J and Levi-Aharoni, H and Le, T and Mifsud, D and Deevi, PI and Nason-Tomaszewski, SR and Pritchard, AL and Zhang, Y and Richards, B and Bechefsky, P and Hochberg, LR and Williams, Z and Shahlaie, K and AuYong, N and Rubin, DB and Pandarinath, C and Brandman, DM and Stavisky, SD},
title = {A generalizable speech neuroprosthesis.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.64898/2026.07.23.739430},
pmid = {42619707},
issn = {2692-8205},
abstract = {Intracortical brain-computer interfaces (BCIs) can restore communication to people with vocal tract paralysis by decoding cortical activity during attempted speech into text. State-of-the-art systems pairing neural-to-phoneme decoders with phoneme-to-word language models have achieved word error rates (WERs) as low as 1%, but only after collecting thousands of sentences of training data. Shortening the data collection process would facilitate scaling this new technology by reducing the time from device implant to high-accuracy communication. Here we introduce a transformer-based decoder model trained jointly across six intracortical speech BCI participants. For every participant - regardless of sex, disease etiology, or attempted speaking strategy - a multi-user model decoded speech more accurately (over 50% lower relative WER on average) than models trained on individual users' data. Notably, the multi-user model could be finetuned on fewer than 200 sentences from a held-out user to achieve a WER below 7%. These results reveal how to pool intracortical data across people to yield more accurate, generalizable, and rapidly-deployable decoding models.},
}

@article {pmid42620125,
year = {2026},
author = {Lyu, D and Allen, L and Pantis, S and Karagoz, EG and Quabs, J and Parvizi, J},
title = {Causal Mapping of Bodily Awareness and Mesoscale Circuit Organization in the Human Cingulate and Precuneus.},
journal = {Research square},
volume = {},
number = {},
pages = {},
pmid = {42620125},
issn = {2693-5015},
abstract = {How the human brain generates subjective bodily experience remains a fundamental question in cognitive neuroscience. The cingulate cortex and precuneus (CC/PCu) have been implicated in bodily awareness and self-related processing, yet the functional architecture and causal relevance of these regions and the circuit mechanisms supporting conscious bodily states remain unclear. Here, we combined intracranial electrical stimulation, first-person reports, causal electrophysiological connectivity mapping and large-scale functional network analyses to investigate the human CC/PCu in 63 individuals. Across 660 stimulation sites, we identified a mesoscale functional mosaic in which the stimulation of neighboring cortical populations, separated by millimeters, gave rise to categorically distinct subjective experiences, ranging from localized sensorimotor sensations to complex integrated bodily states. These phenomenological differences were not explained by anatomical location alone, but by distinct causal connectivity profiles. Sensorimotor-responsive sites preferentially exhibited divergent outgoing connectivity, whereas complex bodily sites showed convergent incoming connectivity from distributed networks. Among these pathways, connectivity between the CC/PCu and posterior insular cortex emerged as a critical determinant of whether local stimulation produced a reportable bodily experience: millimeters away, neighbouring sites lacking this connectivity remained silent. We further identified a right-lateralized network architecture supporting bodily experience across both neuroimaging and electrophysiological connectivity measures. Together, these findings reveal that conscious bodily states may be rooted in fine-grained mesoscale circuits embedded within large-scale brain networks, demonstrating that connectivity architecture, rather than anatomical location alone, determines the capacity of human cortical sites to be involved in conscious and subjective bodily awareness.},
}

@article {pmid42620316,
year = {2026},
author = {He, B and Weng, Y and Luo, P and Xu, Z and Yan, H and Yang, B and He, Q and Lu, J and Yang, X},
title = {Exploring the potential of natural products in treating alopecia areata.},
journal = {Journal of pharmaceutical analysis},
volume = {16},
number = {8},
pages = {101539},
pmid = {42620316},
issn = {2214-0883},
abstract = {Alopecia areata (AA) is an autoimmune disorder characterized by sudden hair loss, affecting millions worldwide. Conventional synthetic drug treatments are often limited by side effects, driving interest in natural alternatives. This review highlights terpenoids, flavonoids, and polyphenols as promising natural compounds that effectively promote hair regrowth in AA. These phytochemicals exert therapeutic effects primarily by activating pro-anagenic signaling pathways, notably the Wnt/β-catenin and phosphoinositide 3-kinase-protein kinase B (PI3K-Akt) pathways, while inhibiting catagen-associated pathways such as transforming growth factor-β (TGF-β), and modulating immune dysregulation through the suppression of Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling. This review has detailed the mechanisms through which these compounds support hair follicle regeneration, restore immune balance, and reduce inflammation. Collectively, the evidence highlights the significant therapeutic potential of natural products for AA, underscoring the need for further translational research.},
}

@article {pmid42622537,
year = {2026},
author = {Rahman, MKM and Shuvo, HMT},
title = {MI recognition by subject specific localised frequency fusion.},
journal = {Journal of medical engineering & technology},
volume = {},
number = {},
pages = {1-19},
doi = {10.1080/03091902.2026.2720510},
pmid = {42622537},
issn = {1464-522X},
abstract = {EEG-based motor imagery (MI) discrimination is widely utilised in real-life applications, such as brain-computer interfaces (BCIs), due to its non-invasive and comparatively cost-effective nature. However, traditional BCI systems typically rely on a uniform, broad frequency band or a standardised set of sub-bands to extract features. Consequently, they fail to account for distinct physiological variability across subjects, leading to significant classification performance degradation. To address this limitation, we propose a novel framework named subject-specific localised frequency fusion (SSLFF). The proposed method systematically searches for and selects optimal frequency sub-bands , while dynamically merging complementary spectral bands. In addition to this subject-specific frequency localisation, the framework integrates a time-localised feature extraction process. Unlike traditional approaches, the proposed method can operate effectively over a broader master frequency band without experiencing performance degradation, as it automatically isolates the optimal spectral parameters for each subject. Overall, the proposed framework achieves a statistically significant improvement in classification accuracy compared to alternative traditional methods, while maintaining exceptional robustness against variations in system parameters. In short, SSLFF try to address the fact that the learning of human brain varies from person to person and it incorporates subject specific tuning not only in the spatial filter and classifier, but also in frequency band selection and localised feature extraction, leading to a superior classification performance.},
}

@article {pmid42623205,
year = {2026},
author = {Wang, F and Zhang, J and Yang, X and Pan, J and Huang, H and He, Y},
title = {An Auditory BCI System Based on Stimulus-Related Semantic Backgrounds for Consciousness Detection.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2026.3718066},
pmid = {42623205},
issn = {1558-2531},
abstract = {Brain-computer interfaces (BCIs) hold significant promise in medical applications, particularly for detecting consciousness in patients with disorders of consciousness (DoC). However, conventional BCIs often rely on visual stimuli, which limits their accessibility for visually impaired patients. To expand the applicability of BCIs to a wider range of patients, this study introduces an advanced auditory BCI system. The system incorporates an auditory paradigm utilizing stimulus-related semantic backgrounds and an improved EEG-Inception prototype network with ECA (ECAEI-ProNet). To validate the effectiveness of the proposed system, Experiment 1 was conducted to compare it against three control conditions: Condition 1, which included related backgrounds and stimuli; Condition 2, which included unrelated backgrounds and stimuli; and Condition 3, which presented no background. The results demonstrated that utilizing stimulus-related semantic backgrounds significantly improved BCI classification performance while eliciting event-related potentials (ERP) patterns associated with semantic consistency in subjects. Additionally, the two improvements made to the ECAEI-ProNet model, building upon EEG-Inception, enhanced the model's classification performance. Our proposed paradigm and classification model achieved online accuracies of 88.8$\pm$ 9.1%. To evaluate the clinical feasibility of the proposed BCI system, we applied it to 17 patients with DoC in Experiment 2. Results indicated that seven patients achieved online accuracy significantly above the chance level ($>$64%). Among them, the three highest-performing patients (P3, P5, and P12) showed improvements in both CRS-R scores and clinical diagnosis at the three-month follow-up. These findings suggest that the proposed auditory BCI system may offer preliminary information relevant to residual consciousness-related processing in some patients with DoC.},
}

@article {pmid42567194,
year = {2026},
author = {Fu, Z and Gong, Z and Shen, Z and Yao, S and Ding, X and Chen, Y},
title = {Slide-DML: sliding-window-based estimation of heterogeneous treatment effects.},
journal = {Physiological measurement},
volume = {47},
number = {8},
pages = {},
doi = {10.1088/1361-6579/ae9713},
pmid = {42567194},
issn = {1361-6579},
mesh = {Treatment Effect Heterogeneity ; *Machine Learning ; Humans ; },
abstract = {Objective.Heterogeneous treatment effect (HTE) estimation is essential for understanding individual differences in physiological responses and supporting personalized healthcare. However, existing non-parametric HTE estimation methods often rely on complex partition strategies and may have limited interpretability when applied to physiological measurements with continuous variations and non-uniform data distributions. This study aims to develop an adaptive and interpretable framework for HTE estimation in physiological measurement systems.Approach.We propose a sliding-window-based double machine learning framework (Slide-DML) for non-parametric HTE estimation. Slide-DML adaptively constructs quasi-homogeneous local windows based on treatment effect variation and estimates local linear HTE within each selected window. The local estimates are subsequently aggregated using adaptive weighting to obtain a smooth global HTE function while preserving interpretability.Main results.The performance of Slide-DML was evaluated using synthetic data, semi-synthetic clinical data, and real physiological measurements. In synthetic experiments, Slide-DML achieved a mean squared error (MSE) of 0.006, outperforming existing machine learning-based HTE estimation methods. In semi-synthetic experiments, Slide-DML achieved a root MSE of 3.526, demonstrating superior performance compared with both machine learning-based and deep learning-based approaches. Experiments on real photoplethysmogram and electrocardiogram data further showed that the estimated treatment effect curves were consistent with established cardiovascular knowledge and provided improved interpretability.Significance.Slide-DML provides an effective and interpretable approach for estimating HTE in physiological measurement systems. By capturing continuous variations in physiological states, the proposed framework may facilitate individualized analysis of cardiovascular responses and support personalized healthcare applications, such as cuffless blood pressure monitoring using wearable physiological devices.},
}

Treatment Effect Heterogeneity
*Machine Learning
Humans

@article {pmid42612453,
year = {2026},
author = {Zuo, X and Sun, X and Cong, L and Wang, H and Liu, S and Zhang, XD},
title = {Flexible bioelectronic electrodes: From engineering strategies to cross-domain biomedical applications.},
journal = {Biosensors & bioelectronics},
volume = {313},
number = {},
pages = {119131},
doi = {10.1016/j.bios.2026.119131},
pmid = {42612453},
issn = {1873-4235},
abstract = {Flexible electrodes have emerged as key bioelectronic interfaces for recording, modulation, and sensing in complex biological environments. Their mechanical compliance, conformal contact, and tunable electrochemical properties enable effective coupling between soft biological tissues and rigid electronic systems. However, the fabrication strategies and interface requirements governing flexible electrode performance differ substantially across biological domains, and this cross-domain design landscape has not been systematically addressed. This review comprehensively examines preparation and optimization strategies for flexible electrodes, covering structure-driven designs, material selection, and surface functionalization approaches targeting charge transfer, selectivity, and biostability. We further survey applications across four biomedical domains: brain interfaces, peripheral nerve interfaces, gastrointestinal tract, and wearable health monitoring, spanning epidermal, visceral, and neural environments in both in vitro and in vivo settings. By mapping design strategies onto diverse biological interface requirements, this review provides a cross-domain reference framework to guide application-specific electrode design. Remaining challenges in scalable fabrication, chronic biointegration, closed-loop operation, and clinical translation are critically discussed, with future directions emphasizing convergence across neuroscience, materials science, and intelligent bioelectronics.},
}

@article {pmid42614398,
year = {2026},
author = {Yu, Z and Cao, S and Zhang, G and Feng, P},
title = {Feedback-regulated dual-role memory consolidation for continual learning: a stability-plasticity framework inspired by hippocampo-cortical systems consolidation.},
journal = {Cognitive neurodynamics},
volume = {20},
number = {1},
pages = {159},
pmid = {42614398},
issn = {1871-4080},
abstract = {Artificial agents trained on non-stationary task streams often acquire new categories at the cost of older representations. Systems-level accounts of hippocampo-cortical consolidation suggest that stored traces can support later learning in different ways and that consolidation can depend on both new learning and the vulnerability of established knowledge. Motivated by these functional principles, we propose dual-role memory consolidation learning (DRMCL) for class-incremental learning with a bounded replay buffer. DRMCL stores prototype-preserving core samples and decision-sensitive boundary samples, then combines both roles with replay, logit distillation, and task-level feedback control. On Split CIFAR-10 with a pretrained ResNet18 backbone, the clearest benefit occurs when memory is scarce. With 1000 stored samples, average accuracy increases from 0.4823 for Dark Experience Replay++ (DER++) to 0.6905, while forgetting decreases from 0.5734 to 0.2650. At medium and high memory, DRMCL usually lowers forgetting, although DER++ can achieve higher raw accuracy. Ablations show that feedback-regulated replay and distillation account for most of the retention effect, while the core/boundary organization makes the selected memory easier to inspect. An electroencephalography (EEG) brain-computer interface (BCI) feasibility study on BCI Competition IV 2a provides a cross-domain check. In a five-seed subject-1 experiment, DRMCL remains close to ER and DER++ rather than separating from them, and session spectral/coherence shifts vary across subjects. The results support DRMCL as a stability-oriented computational framework; they do not establish a circuit-level model of hippocampo-cortical consolidation or a new EEG decoding benchmark.},
}

@article {pmid42615121,
year = {2026},
author = {Rahman, MH and Kadri, HJ and Ahmed, F and Nuruzzaman, M and Mondal, MIH},
title = {Fabrication & characterization of neem oil-loaded hydrophobic PVA film for rapid hemostasis and minimizing blood loss in wound dressing.},
journal = {Biomedical materials (Bristol, England)},
volume = {},
number = {},
pages = {},
doi = {10.1088/1748-605X/ae9b57},
pmid = {42615121},
issn = {1748-605X},
abstract = {The objective of this investigation is to develop a dressing film with a blood-repellent surface to improve hemostatic performance while minimizing excessive blood loss from absorption and potentially reducing clot-dressing adhesion. As a result, a modified technique was utilized to fabricate a hydrophobic film made of PVA and neem seed oil. The developed film underwent multiple physicochemical studies to determine its structure and properties. The films exhibited increasing hydrophobicity with neem oil incorporation, as evidenced by water contact angle values rising from 47.8° (pure PVA) to 93.56° (PVA with 4% NSO), which suggests a potential reduction in adhesion risk. Kirby-Bauer agar diffusion assays demonstrated inhibition zones of up to 14 ± 0.1 mm against Pseudomonas aeruginosa, indicating preliminary antibacterial activity. Hemostatic evaluation revealed enhanced red blood cell and platelet adhesion, with the optimized PVA/NSO (A4) film exhibiting a blood clotting index (BCI) approximately 28% lower than that of the gauze control, indicating enhanced in vitro clotting efficiency compared with the gauze control. Qualitative assessment demonstrated that the films maintained good flexibility and structural integrity under bending, twisting, and stretching. Biodegradation studies showed weight loss of 25.7% for PVA/NSO films after 28 days, confirming environmental compatibility. Collectively, these results demonstrate that PVA/NSO composite films possess hydrophobicity, antibacterial activity, mechanical stability, and superior hemostatic performance, making them promising candidates for wound dressing and rapid hemostasis applications.},
}

@article {pmid42615139,
year = {2026},
author = {Conte, G and Borghi, F and Lazzarini, C and Piazzoni, C and Konstantoulaki, A and Fabbri, R and Caprini, M and Milani, P and Benfenati, V},
title = {Nanostructured Zirconia Thin Films as a Neurogliomorphic Interface for Neural Cells of Central and Peripheral Nervous Systems.},
journal = {ACS applied bio materials},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsabm.6c01280},
pmid = {42615139},
issn = {2576-6422},
support = {ASTROSENSE FA9550-25-1-0001//Air Force Office of Scientific Research/ ; ASTROTALK - FA9550-23-1-0736//Air Force Office of Scientific Research/ ; W911NF2520009_ ASTROCLUSTER//Army Research Office/ ; CUP: E29I25000730007//Regione Lombardia/ ; ECS_00000033_ECOSISTER//Ministero dell'Università e della Ricerca/ ; },
abstract = {Recent advances in neuroscience have highlighted the central role of glial cells, particularly astrocytes, in regulating neural network activity through calcium-dependent neuron-glia communication. In parallel, nanostructured cluster-assembled materials have emerged as promising candidates for developing brain-machine interfaces because of their biomimetic morphology, mechanotransductive properties, and neuromorphic behavior. Among these, nanostructured zirconium oxide (ns-ZrOx) thin films have recently demonstrated memristive and signal-processing capabilities compatible with biohybrid neural systems, yet their interaction with heterogeneous neuroglial networks remains poorly understood. Here, we investigate the biocompatibility and functional effects of ns-ZrOx interfaces on primary astrocytes and dorsal root ganglion (DRG) neuron-glia co-cultures, comparing nanostructured and flat zirconia substrates. Both substrates supported cellular adhesion, survival, and differentiation. However, ns-ZrOx selectively enhanced glial calcium signaling, increasing transient amplitude and accelerating response kinetics in both central and peripheral glial populations. Our findings identify ns-ZrOx as an active neurogliomorphic interface capable of modulating neuron-glia communication through nanoscale material properties. By bridging glial physiology with neuromorphic nanomaterials, this work supports the development of hybrid bioelectronic platforms integrating living neural networks with adaptive functional materials for brain-inspired computing and advanced neural interfaces.},
}

@article {pmid42611651,
year = {2026},
author = {Wang, Z and Liu, Y and Huang, S and Wu, W and Huang, H and Li, Z and Zhang, H and Guo, J and Xu, M and Ming, D},
title = {ERD Full-process Longitudinal Trend and Pre-post Motor Recovery Under BCI-controlled Sixth-finger Neurofeedback Intervention in Stroke Patients: an Exploratory Single-arm Study.},
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.2026.3724844},
pmid = {42611651},
issn = {1558-0210},
abstract = {Motor imagery-based brain-computer interface (MI-BCI) is used in stroke rehabilitation to match brain activity with contingent feedback to establish closed-loop pathways and provide a measure of neuroplasticity changes in patients. However, most studies assessed neural function only at pre- and post-train, thereby longitudinal trends of neural patterns and mechanisms during full-process of intervention remain unclear. Fourteen stroke patients were recruited to receive a total of 8-session (2-week) MI-BCI-controlled "sixth-finger" intervention. Resting-state electroencephalography (EEG) and clinical scales, including the Fugl-Meyer Assessment (FMA-UE) and Barthel Index (BI), were evaluated pre- and post-train. Furthermore, MI tasks EEG signals throughout the full-process of intervention were tracked to reflect the longitudinal continuous trends of neural activity. EEG longitudinal trend shows two phases over full-process of intervention: event-related desynchronization (ERD) gradually increased in the first week of training, weakened and focused on the contralateral sensorimotor area in the second week, and showed a significant correlation over sessions. And resting-state functional connectivity increased after intervention. Motor function improved significantly from pre- to post-train by clinical metrics, with + 7.9 in FMA-UE and + 7.1 in BI. More than half of patients (9/14) reached the minimally clinically important difference (MCID) of 6.6 points change for FMA-UE after therapy. Meanwhile, the improvement of motor function is associated with the enhancement of resting-state functional connectivity. This work reveals longitudinal trend of neural patterns over full-process of intervention and its correlation with motor recovery, providing crucial evidence for understanding the mechanisms of neuroplasticity in stroke rehabilitation.},
}

@article {pmid42611663,
year = {2026},
author = {Wu, H and Zhang, G and Liao, J and Zhang, J and Zheng, WS and Long, J},
title = {Calibration-free Plug-and-Play EEG-based BCIs.},
journal = {IEEE transactions on pattern analysis and machine intelligence},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TPAMI.2026.3725079},
pmid = {42611663},
issn = {1939-3539},
abstract = {Calibration-free plug-and-play operation is critical for real-world EEG-based brain-computer interfaces (BCIs), yet existing methods demand subject-specific calibration or batch processing of target data. To overcome this, we propose Local Online Transfer Learning (LOTL), a novel online transfer learning method enabling effective and immediate response without calibration. Specifically, for each domain (subject), LOTL generates a global hyperplane and multiple local hyperplanes by considering the nonlinear separation problem of samples. During online processing, LOTL attempts to update the current model by retaining the new model close to the current source models and the target model while imposing a margin separation on the latest samples. This is achieved by minimizing the differences between the current and new global and local hyperplanes of the source and target domains, making the learned global and local hyperplanes of the source and target domains transferable. Crucially, LOTL works in a one-pass online manner, using each arriving sample only once without storing any historical samples. We derive a closed-form solution for effective model updates and establish theoretical guarantees mathematically, including mistake bound and convergence analyses. Extensive experiments on four EEG datasets (motor imagery and emotion analysis) and a real-world BCI system provide encouraging evidence for the effectiveness of the proposed method, suggesting the potential of LOTL to facilitate the deployment of BCI applications under conditions where the source and target domains share a consistent channel configuration.},
}

@article {pmid42612138,
year = {2026},
author = {Li, K and Shen, Z and Lu, H and Fang, X},
title = {Sleep Deprivation, Decision Fatigue, and Clinical Performance in Medical Training: A Narrative Review and Perspective on Neurophysiological Monitoring.},
journal = {Journal of visualized experiments : JoVE},
volume = {},
number = {233},
pages = {},
doi = {10.3791/71627},
pmid = {42612138},
issn = {1940-087X},
mesh = {*Sleep Deprivation/physiopathology ; Humans ; *Fatigue/physiopathology ; *Decision Making/physiology ; *Neurophysiological Monitoring/methods ; Electroencephalography/methods ; *Clinical Competence ; *Education, Medical/methods ; Spectroscopy, Near-Infrared/methods ; },
abstract = {Sleep deprivation and decision fatigue are important but often under-recognized determinants of daytime clinical performance. In medical training environments, these state-dependent factors may impair attention, executive control, and decision-making, complicating the interpretation of observed competence during wakefulness. This narrative review and perspective synthesizes evidence from sleep science, neurophysiology, psychology, and medical education to examine how sleep loss and decision fatigue shape clinical performance and how non-invasive monitoring approaches may help contextualize this variability. Attention is given to electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as candidate research tools for characterizing fatigue-related changes in cognitive workload and vigilance. The reviewed literature supports strong links between sleep loss, fatigue, and impaired neurocognitive functioning, but the use of real-time neurophysiological monitoring for actionable readiness assessment in medical training remains conceptual and requires empirical validation. We propose the CBME-BCI-Fatigue framework as a hypothesis-generating perspective model for integrating physiological state information with competency-based assessment, while emphasizing limitations related to construct validity, incremental value, individual variability, feasibility, and governance. This framework may inform future simulation-based and low-stakes research on fatigue-aware feedback and learner support in sleep-vulnerable clinical training environments.},
}

*Sleep Deprivation/physiopathology
Humans
*Fatigue/physiopathology
*Decision Making/physiology
*Neurophysiological Monitoring/methods
Electroencephalography/methods
*Clinical Competence
*Education, Medical/methods
Spectroscopy, Near-Infrared/methods

@article {pmid42612169,
year = {2026},
author = {Wang, J and Zhang, Y and Van Cang, MP and Ding, N and Morillon, B and Doelling, KB},
title = {Music Is Quasi-Rhythmic Too: Comparing Bandwidth in Speech and Music Acoustic Modulation Spectra.},
journal = {Annals of the New York Academy of Sciences},
volume = {1562},
number = {1},
pages = {e70380},
pmid = {42612169},
issn = {1749-6632},
support = {//Fundamental Research Funds for the Central Universities/ ; //Fondation Fyssen/ ; //Fondation Pour l'Audition/ ; //French government/ ; //European Union/ ; },
mesh = {Humans ; *Music ; *Periodicity ; *Speech/physiology ; *Speech Perception/physiology ; *Auditory Perception/physiology ; *Acoustics ; Acoustic Stimulation/methods ; },
abstract = {Speech and music both unfold over time. However, these dynamics are perceived quite differently: music as rhythmic, and speech as quasi-rhythmic. Here, we aim to identify whether this distinction between rhythm and quasi-rhythm is sourced from the raw acoustic waveform by analyzing the modulation spectrum of the acoustic envelope. We analyzed several large corpora on three scales: the coarsest scale containing recordings of each corpus, the intermediate scale of individual speakers/songs, and the finest scale of individual sentences or short musical segments. We confirm previous findings that speech and music modulation spectra differ in their center frequency, which reflects how quickly the sound envelope fluctuates, but find that the modulation bandwidth, which captures how periodic the speech/music envelope was, is comparable for music and speech. Furthermore, the bandwidth is largely preserved across the three scales, indicating that the corpus-level temporal irregularity is dominated by the irregularity within a few seconds of speech/music recordings. These results demonstrate that the perceived rhythmicity of music may not directly reflect the temporal periodicity present in its acoustic structure.},
}

Humans
*Music
*Periodicity
*Speech/physiology
*Speech Perception/physiology
*Auditory Perception/physiology
*Acoustics
Acoustic Stimulation/methods

@article {pmid42612357,
year = {2026},
author = {Xiao, X and Sheng, X and Li, S and Chen, X and Zhang, Z and Si, K and Gong, W},
title = {Dual-mode thermo-responsive microneedle liposome patch for adaptive transdermal delivery in postherpetic neuralgia.},
journal = {Biomaterials advances},
volume = {189},
number = {},
pages = {215121},
doi = {10.1016/j.bioadv.2026.215121},
pmid = {42612357},
issn = {2772-9508},
abstract = {Postherpetic neuralgia (PHN) presents as persistent background pain accompanied by unpredictable breakthrough episodes. Current topical therapies are poorly suited to this fluctuating pain pattern because they provide limited transdermal penetration and static drug release. Here, we developed a thermo-responsive microneedle platform integrating sustained local lidocaine delivery with externally triggered accelerated release and evaluated its material characteristics, temperature-dependent release behavior, transdermal delivery performance, and in vivo pharmacokinetic behavior. The system consists of a gelatin and poly (ethylene glycol) diacrylate microneedle matrix, lidocaine-loaded liposomes, and a flexible pullulan backing layer. The patch demonstrated adequate mechanical strength for skin insertion, exceeding 0.1 N per needle, achieved an insertion efficiency above 93%, and delivered cargo to a depth of approximately 75 μm near the epidermal-dermal interface. Compared with a conventional topical patch, the microneedle liposome configuration enhanced transdermal permeation and prolonged drug retention in the skin for up to 48 h, enabling both rapid initial delivery and sustained local availability. The system exhibited a dual-mode release profile, with sustained release at physiological skin temperature (33 °C) and accelerated release under mild heating (40 °C), allowing externally triggered control of release kinetics. The flexible backing maintained conformal adhesion under dynamic deformation, and skin evaluation indicated minimal barrier disruption with only mild, transient erythema in human subjects. These findings demonstrate the feasibility of the system as a controlled local delivery platform and support further evaluation of the system in disease-relevant PHN models.},
}

@article {pmid42484995,
year = {2026},
author = {Tahmasebi, G and Vargas, S and Kuete, CF and Haghighi, P and Solis, E and Massaquoi, A and Pancrazio, JJ},
title = {Sigmoidal decoding from distinct M1 spiking populations and LFP band power for locomotion speed in mice.},
journal = {Journal of neurophysiology},
volume = {136},
number = {2},
pages = {807-823},
doi = {10.1152/jn.00112.2026},
pmid = {42484995},
issn = {1522-1598},
support = {R01NS131502//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; },
mesh = {Animals ; Mice ; *Motor Cortex/physiology/cytology ; *Action Potentials/physiology ; Local Field Potential Measurement ; Male ; *Locomotion/physiology ; *Neurons/physiology ; Models, Neurological ; Mice, Inbred C57BL ; Female ; },
abstract = {The extent to which the primary motor cortex (M1) encodes locomotion speed is relatively unexplored, with some studies suggesting linear or gain-modulated tuning. Here, we show that there are neurons in the mouse M1 where spiking relates to locomotion speed in a manner consistent with a sigmoidal state-transition model implemented by two functionally distinct neural populations. In addition, sigmoidal framework extends to local field potential (LFP) band power, enabling a direct within-animal comparison of spiking and LFP-based speed encoding. We recorded extracellular activity (5,889 single units, 384 channels) using chronic 32-channel laminar arrays in eight mice locomoting on a motorized treadmill over 8 wk, with actual speed tracked via DeepLabCut. Unsupervised clustering of temporal firing rate profiles identified two groups: speed-positively related (70.8%) and speed-inversely related (29.2%) units. Sigmoidal models of speed-positively related rate tuning significantly outperformed linear and quadratic alternatives for both populations, and the two clusters shared a common speed threshold (∼2.3 m/min) consistent with a shared subcortical locomotor gate. When exploring decoding, the minority speed-inversely related population demonstrated significantly higher decoding accuracy via inverse-sigmoid transformation compared with the larger speed-positively related population or all units combined, an advantage that generalized across animals via leave-one-animal-out cross-validation. LFP band power also exhibited sigmoidal tuning relative to locomotion speed, but decoded speed with lower fidelity. These findings suggest a push-pull sigmoidal architecture for spiking-based speed representation in M1 and demonstrate that LFP provides a complementary and stable signal for coarser speed estimation.NEW & NOTEWORTHY This study reveals that locomotion speed is encoded in mouse primary motor cortex through a sigmoidal state-transition mechanism carried by two functionally distinct spiking populations with a shared speed threshold. Using high-density laminar recordings and markerless motion tracking, we show that this framework extends to local field potentials and enables accurate, generalizable decoding. These findings establish a push-pull sigmoidal architecture and highlight implications for stable, calibration-light brain-machine interface design.},
}

Animals
Mice
*Motor Cortex/physiology/cytology
*Action Potentials/physiology
Local Field Potential Measurement
Male
*Locomotion/physiology
*Neurons/physiology
Models, Neurological
Mice, Inbred C57BL
Female