Journal of neural engineering最新文献

{"title":"Explainable fMRI decoding reveals neurobiological signatures of emotional arousal complementary to traditional analyses.","authors":"Daniel Agostinho, Davide Borra, Miguel Castelo-Branco, Marco Simões","doi":"10.1088/1741-2552/aead27","DOIUrl":"10.1088/1741-2552/aead27","url":null,"abstract":"<p><p><i>Objective.</i>Understanding how emotional arousal is represented in the brain is fundamental for elucidating affective regulation and its alterations in autism spectrum disorder. Classical neuroimaging approaches, such as the general linear model (GLM), have provided important insights into emotional processing. However, the extent to which data-driven decoding and traditional model-based analyses provide complementary information about emotional arousal remains unclear.<i>Approach.</i>We introduce an explainable artificial intelligence framework that combines the lightweight functional magnetic resonance imaging (fMRI) decoder (fMRINet) with Deep Learning Important FeaTures to derive data-driven neurobiological signatures of emotional arousal. The framework enables interpretation of spatiotemporal neural contributions during emotional processing. We applied the framework to fMRI data acquired while neurotypically and autistic participants viewed naturalistic emotional videos and compared the resulting signatures with categorical and parametric GLM analyses.<i>Main results.</i>The framework identified distinct spatiotemporal neurobiological signatures associated with emotional arousal. Neurotypical participants engaged a distributed perceptual-limbic-prefrontal network under high arousal and frontoparietal executive regions under low arousal. In contrast, autistic participants exhibited a more spatially restricted and posterior-biased pattern under high arousal, with reduced ventromedial prefrontal and limbic contributions, while low-arousal patterns resembled typically developing, suggesting preserved executive mechanisms during low-arousal processing. The GLM analyses revealed partly convergent and partly distinct findings: categorical GLM detected no significant group differences, whereas parametric GLM identified widespread cortical-subcortical modulation in neurotypical individuals and cerebellar-restricted effects in autistic, consistent with compensatory reliance on motor-timing circuits.<i>Significance</i>. The proposed framework provides an interpretable, data-driven perspective on emotional processing and reveals neurobiologically plausible patterns consistent with existing literature. Together, the explainable decoding and GLM results highlight complementary aspects of emotional arousal processing, suggesting that explainable decoding approaches may provide additional insights alongside traditional model-based neuroimaging analyses.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149077233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hierarchical graph learning framework for VR-based motor imagery decoding driven by MVMD and feature optimization.","authors":"Kaiyue Du, Wenwen Chang, Weixuan Kong, He Huang, Guanghui Yan, Muhammad Tariq Sadiq","doi":"10.1088/1741-2552/aeabf5","DOIUrl":"10.1088/1741-2552/aeabf5","url":null,"abstract":"<p><p><i>Objective</i>. While virtual reality (VR) combined with action observation (AO) provides enriched visual guidance for motor imagery (MI), the decoding stability of MI Brain-Computer Interface (BCI) remains a challenge due to the inherent non-stationarity and low signal-to-noise ratio of Electroencephalography (EEG) signals in dynamic environments. Enhancing decoding accuracy and understanding neural representations in VR-based AO + MI tasks remain important challenges in neural engineering.<i>Approach.</i>This study proposes a hierarchical graph learning framework tailored for VR-based MI decoding, leveraging multivariate variational mode decomposition to decouple complex EEG signals into rhythmic components for enhanced multi-domain feature analysis, hybrid feature selection for compact representation, and functional brain network modeling based on phase locking value. Each EEG trial is represented as a sparsified graph, and a hierarchical graph convolutional network is designed to capture multi-scale spatial dependencies and high-order interactions for classification.<i>Main results</i>. Experimental evaluations on a lab-acquired virtual reality-based motor imagery dataset demonstrate that the proposed framework achieves competitive three-class decoding accuracy. This performance surpasses baseline models such as EEGNet, as observed under VR-based MI conditions. In addition to enhanced decoding performance, the study enables neurophysiological interpretation by characterizing VR-induced modulations in brain network organization.<i>Significance</i>. VR-based AO + MI paradigm is found to enhance sensorimotor connectivity and promote large-scale network integration, indicating more coordinated neural dynamics during MI tasks. These findings suggest that the proposed framework effectively improves VR-based AO + MI decoding while providing interpretable insights into neural mechanisms, supporting its potential for advanced BCI applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149032290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AOR: adaptive orthogonal regression for depression feature selection with incomplete EEG channels.","authors":"Wenjia Dong, Zhijian Gong, Xueyuan Xu, Ke Liu, Fulin Wei, Li Zhuo","doi":"10.1088/1741-2552/aeb32f","DOIUrl":"https://doi.org/10.1088/1741-2552/aeb32f","url":null,"abstract":"<p><strong>Objective: </strong>Depression induces severe mental and physical impairments, highlighting the&#xD;clinical need for rapid and reliable detection. Electroencephalography (EEG) directly reflects&#xD;neural electrical activity and serves as a promising tool for depression investigation. However,&#xD;the inherent non-stationarity, redundancy, and noise susceptibility of EEG signals, together&#xD;with the common issue of incomplete channel data due to electrode loss or severe noise during&#xD;acquisition, compromise the reliability of diagnostic models. Existing EEG feature selection&#xD;methods lack adaptability to such incomplete data, leading to distorted feature evaluation and&#xD;degraded performance.</p><p><strong>Approach: </strong>To address this limitation, an adaptive orthogonal regression&#xD;(AOR) method is proposed for EEG feature selection for depression detection under incomplete&#xD;channel conditions. The method integrates three core innovations: embedding missing channel&#xD;information into the orthogonal regression formulation to mitigate the impact of incomplete&#xD;channels, incorporating adaptive channel-weighted learning to automatically quantify the&#xD;relative importance of different channels, and applying global redundancy minimization&#xD;to eliminate inter-feature redundancy while enhancing discriminability. The optimization&#xD;problem is solved via Generalized Power Iteration and Augmented Lagrangian Multiplier&#xD;methods.</p><p><strong>Main results: </strong>AOR was validated on the MODMA and PRED-d003 datasets across&#xD;3-channel, 64-channel, and 128-channel configurations under single-modal and multi-modal&#xD;scenarios. Experimental results demonstrate that AOR consistently outperforms state-of-the art methods across various missing ratios and channel configurations. The average cross-subject&#xD;depression recognition accuracy reaches 92.25 % and 95.12 % across different missing ratios on&#xD;the two datasets. Additionally, feature weight analysis reveals that EEG kurtosis consistently&#xD;exhibits high discriminative power across different configurations.</p><p><strong>Significance: </strong>The proposed&#xD;AOR method provides a robust and efficient solution for depression feature selection under&#xD;incomplete EEG channel conditions, offering reliable diagnostic performance in real-world&#xD;clinical scenarios.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined voltammetry and optical brain imaging: toward high resolution neurochemical measurements.","authors":"Vladislav Zavialov, Vassiliy Tsytsarev, Anna Volnova, Raul R Gainetdinov, Mikhail Inyushin","doi":"10.1088/1741-2552/aeb32e","DOIUrl":"https://doi.org/10.1088/1741-2552/aeb32e","url":null,"abstract":"<p><p>The question of the mechanisms by which cellular interactions in the brain are converted into integrative behavioral phenomena is closely related to functional localization. Electrophysiology is usually limited in its ability to display information about the functioning of neural circuits with sufficient spatial resolution, although the temporal resolution is almost always very high. Other modern methods for recording the functional characteristics of neural activity in the brain vary considerably in spatial and temporal resolution.&#xD;Voltammetry is one of the key instrumental methods used in neurobiology to measure neurotransmitter release and dynamics in vivo. It is similar to electrophysiology in terms of temporal resolution, but spatial resolution remains low. This method records changes in the concentration of chemicals, typically neurotransmitters, in the brain's extracellular environment with sufficient temporal resolution to capture release peaks. However, as this monitoring is performed at a single location or target, the technique does not provide information about the spatial distribution of the recorded signal characteristics.&#xD;Various techniques for functional optical imaging of brain structure and activity offer relatively high spatial resolution, often accompanied by adequate temporal resolution. Some of these methods are suitable for visualizing neural activity in both 2D and 3D. However, while functional brain imaging allows areas of neural activity to be identified, it typically does not provide quantitative information about changes in the concentration levels of specific molecules.&#xD;A significant challenge would be to combine the key advantages of voltammetry and functional optical brain imaging, i.e., to obtain continuous quantitative information on the concentration of neurochemically active molecules across many areas of the investigated brain region with sufficiently high temporal and spatial resolution. This review discusses the current state of voltammetry and functional brain imaging techniques, as well as the hypothetical possibilities for combining them based on the experimental data obtained to date.&#xD;&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MTDANet: lightweight multi-scale temporal hierarchy and distance-regularized linear attention network for motor imagery brain-computer interfaces.","authors":"Binbin Chen, Chen Deng, Huanmin Ge, Xinhua Su, Yanfei Shen","doi":"10.1088/1741-2552/aeb330","DOIUrl":"https://doi.org/10.1088/1741-2552/aeb330","url":null,"abstract":"<p><strong>Objective: </strong>Motor-imagery (MI) electroencephalography (EEG) offers a non-invasive route to brain-computer interaction, yet reliable single-trial decoding remains difficult because task-related patterns are weak, nonstationary, and distributed over multiple temporal scales. This study investigates whether a staged local-to-global model can integrate fine temporal-spatial structure with longer-range context without relying on unrestricted self-attention.</p><p><strong>Approach: </strong>We propose MTDANet, an architecture that combines multi-scale temporal-spatial feature extraction, factorized temporal consolidation, and distance-regularized recurrent attention. The model was evaluated on three public MI datasets under subject-specific and leave-one-subject-out (LOSO) protocols. Separately initialized models were also trained on P300 and steady-state visual evoked potential (SSVEP) datasets to examine broader architectural applicability. Participant-level paired analyses used two-sided paired t tests with within-setting Holm correction.</p><p><strong>Main results: </strong>MTDANet achieved subject-specific accuracies of 85.61 ± 8.32%, 90.85 ± 7.62%, and 87.79 ± 13.93% on BCI Competition IV 2a, BCI Competition IV 2b, and BNCI2014_002, respectively. Relative to the highest-mean baseline, the paired improvements were supported after within-setting Holm correction on BCI Competition IV 2a (3.56 percentage points, adjusted p = 0.0097) and 2b (1.62 points, adjusted p = 0.0168). Under LOSO evaluation, the corresponding accuracies were 63.31 ± 15.74%, 79.77 ± 7.57%, and 79.96 ± 11.95%. The 2.08-point BNCI2014_002 difference had a 95% confidence interval of 0.42-3.73 points and an adjusted p value of 0.0387. These three comparisons did not remain significant in a broader 56-comparison Holm sensitivity analysis. The P300 and SSVEP models attained the highest observed mean accuracies.</p><p><strong>Significance: </strong>The results provide within-setting evidence for staged local-to-global temporal modeling in offline MI-EEG evaluations.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GROND: Automated characterization of periodic and rhythmic EEG patterns.","authors":"Jin Jing, Chenxi Sun, Tianyu Zhang, Matthew Byrd, Alexandra-Maria Tăuțan, Lara Basovic, Peter N Hadar, Marta P Fernandes, Daniel M Goldenholz, Jennifer Kim, Aaron F Struck, Sahar F Zafar, Brandon Westover","doi":"10.1088/1741-2552/aeb331","DOIUrl":"https://doi.org/10.1088/1741-2552/aeb331","url":null,"abstract":"<p><strong>Objective: </strong>Periodic discharges (PDs) and rhythmic delta activity (RDA) are common EEG patterns in critically ill patients that require characterization of lateralization, spatial localization, and frequency. Manual characterization is subjective and time-consuming. We present an automated system for all four major subtypes: lateralized and generalized PDs (LPD, GPD) and lateralized and generalized RDA (LRDA, GRDA).&#xD;&#xD;Approach. The PD-Profiler combines a per-channel convolutional neural network, hemisphere-specific learned evidence traces, dynamic programming with an approximately periodic prior, and discharge-locked topographic localization. The RDA-Profiler uses iterative narrowband Hilbert refinement for frequency and lateralization, with phase-locking-value analysis for spatial extent. Both pipelines were trained and evaluated on 12,425 EEG segments from 11,729 patients.&#xD;&#xD;Main results. Four electroencephalographers independently scored 200 stratified segments per subtype. The algorithm exceeded expert-expert (EE) agreement on LPD frequency (expert-algorithm (EA) ICC 0.931 versus EE 0.897; p<0.001) and was statistically indistinguishable from EE on GPD frequency (EA 0.980 versus EE 0.973; p=0.061), GRDA frequency (p=0.056), LRDA frequency (p=0.435), LPD laterality (EA kappa 0.945 versus EE 0.974; p=0.079), and LRDA laterality (EA kappa 0.961 versus EE 0.993; p=0.269). No attribute fell significantly below the EE ceiling. PD discharge timing achieved an F1 of 0.817 at single-sample precision (mean absolute timing error 3.1 ms). PD spatial localization reached 97.3% of expert inter-rater Jaccard agreement, and RDA spatial extent matched EE agreement.&#xD;&#xD;Significance. GROND reaches expert-level reliability across lateralization, spatial localization, discharge timing, and frequency for PDs and RDA. In discordant cases, experts judged its frequency estimates more accurate than the original labels in 94% of cases, supporting reproducible characterization at scale.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal regulation of neurite extension by extracellular matrix composition and electrical stimulation on aligned collagen fiber scaffolds.","authors":"Madalynn Thompson, Joanna Borowska-Fielding, Ivy Murphy, Ying Zhang, Victor Rafuse, John P Frampton","doi":"10.1088/1741-2552/aeb32c","DOIUrl":"https://doi.org/10.1088/1741-2552/aeb32c","url":null,"abstract":"<p><p>Peripheral neurons respond to structural, biochemical, and electrical cues to regenerate following injury. However, how these cues contribute individually and in combination to promoting neurite outgrowth remains incompletely understood. Here, we developed an aligned collagen fiber-based scaffold to examine how combinations of extracellular matrix proteins contribute to neurite outgrowth with and without electrical stimulus. Type I collagen fibers were fabricated by contact-drawing, a dry spinning process capable of producing collagen fibers that can be twisted into multifilaments that support anisotropic cell growth. A custom frame enabled immobilization of the collagen multifilaments and quantification of longest neurite length. Aligned collagen scaffolds significantly enhanced neurite outgrowth and alignment compared to isotropic controls.Incorporation of fibronectin or collagen IV increased neurite elongation in a concentrationdependent manner, with the longest neurites observed at intermediate ligand densities. Laminin I did not significantly alter neurite length. Combinatorial presentation of fibronectin and collagen IV further demonstrated non-linear responses, with low-concentration combinations enhancing neurite extension and high-concentration combinations providing no additional benefit. Electrical stimulation increased neurite length on collagen-only scaffolds but exhibited diminished or negative effects when applied to ECM-enriched multifilaments, suggesting context-dependent integration of biochemical cues and electrical stimulus. Together, these findings demonstrate that scaffold anisotropy establishes directional guidance, whereas ECM composition and electrical stimulation modulate neurite extension in a non-additive manner. The collagen multifilament system functions as a tunable in vitro platform for systematically interrogating multimodal regenerative cues and defining design principles for next-generation nerve guidance biomaterials.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The challenge of out-of-distribution detection in motor imagery BCIs.","authors":"Merlijn Quincent Mulder, Matias Valdenegro-Toro, Andreea Ioana Sburlea, Ivo Pascal de Jong","doi":"10.1088/1741-2552/aeac87","DOIUrl":"10.1088/1741-2552/aeac87","url":null,"abstract":"<p><p><i>Objective</i>. Machine learning classifiers used in Brain-Computer Interfaces (BCIs) make classifications based on the distribution of examples on which they were trained. When exposed to electroencephalography (EEG) from unfamiliar classes they can only make blind guesses. Instead of allowing such guesses, these out-of-distribution (OOD) samples should be detected and rejected to improve the robustness of BCIs against unfamiliar cognitive states.<i>Approach</i>. We study OOD detection in Motor Imagery BCIs by training a model on some classes and observing whether an unfamiliar movement class can be detected based on increased uncertainty. We tested seven different OOD detection methods and one more method that has been claimed to boost the quality of OOD detection.<i>Main results</i>. For many BCI users, the uncertainty for the familiar in-distribution classes can still be higher than for the OOD classes, due to the high intrinsic variability inherent in EEG signals. As a result, many OOD detection methods that have shown good performance in other machine learning domains prove to be ineffective at identifying unfamiliar motor imagery patterns in BCIs. However, we also found that OOD detection performance is correlated with on-task performance, and that Deep Ensemble models and Monte Carlo-Dropout models were able to achieve on-task Area Under the Receiver-Operator Characteristic curve0.9, and OOD detection ability up to 0.7. This shows that for models and subjects where task performance is high, rejecting unfamiliar cognitive states becomes feasible.<i>Significance</i>. Our research demonstrates a Leave-One-Class-Out OOD detection setup as an experimental paradigm for studying whether models are robust against unfamiliar motor imagery actions that were not seen in the training data. This is the first benchmark in Motor Imagery BCI that evaluates this type of OOD data. Our results demonstrate how to improve the overall safety and reliability of BCIs by preventing erroneous actions.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural coupling between spinal motor neurons of the first dorsal interosseous muscle during individual index finger flexion and pinch tasks.","authors":"Elmira Pourreza, Hélio V Cabral, Nijia Hu, J Greig Inglis, Mikaël Desmons, Ioannis Delis, Laura McPherson, Francesco Negro","doi":"10.1088/1741-2552/aea59d","DOIUrl":"10.1088/1741-2552/aea59d","url":null,"abstract":"<p><p><i>Objective.</i>Precision grip tasks require complex coordination of intrinsic hand muscles, yet how common synaptic inputs to motor neurons are modulated across functionally different tasks remains unclear. This study investigated whether neural coupling between motor unit spike trains in the first dorsal interosseous (FDI) muscle differs between isolated index finger flexion and precision pinch tasks.<i>Approach.</i>Sixteen healthy participants performed isolated index finger flexion and pinch tasks at 10% and 20% of maximal voluntary contraction while high-density surface electromyography was recorded from the FDI. Motor unit spike trains were decomposed and tracked across tasks. Neural coupling was assessed using complementary methods: coherence analysis and proportion of common input (PCI) index to quantify linear common oscillations in delta (1-5 Hz), alpha (5-15 Hz), and beta (15-35 Hz) frequency bands, and mutual information (MI)-based network analysis to capture nonlinear interactions.<i>Main Results.</i>Coherence analysis and PCI revealed no significant differences between tasks across all frequency bands. In contrast, network density derived from MI analysis showed significantly stronger nonlinear motor unit coupling during pinch compared to isolated finger flexion (<i>p</i>= 0.013), independent of force level.<i>Significance.</i>These findings demonstrate a dissociation between linear and nonlinear measures of motor unit coupling. In particular, precision pinch tasks appear to involve greater nonlinear dependencies among motor unit discharge patterns, potentially reflecting higher-order common inputs and distinct neural control strategies not fully captured by traditional linear coherence measures. This highlights that functionally relevant precision behaviors engage additional layers of nonlinear neural coupling, offering new insight into how the nervous system adaptively modulates motor unit coordination to meet complex task demands.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148932284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An implantable wireless battery-free spatially selective vagus nerve stimulator.","authors":"Edvards Rutkovskis, Enrico Ravagli, Henry Lancashire, Ahmad Shah Idil, Nicole Thompson, Justin Perkins, Ronald Challita, Joseph Hadaya, Umesh Vivekananda, Olujimi A Ajijola, Kalyanam Shivkumar, Anna Miserocchi, Andy McEvoy, David Holder, Kirill Aristovich","doi":"10.1088/1741-2552/aea710","DOIUrl":"10.1088/1741-2552/aea710","url":null,"abstract":"<p><p><i>Objective.</i>Vagus nerve stimulation (VNS) is an established clinical therapy for drug-resistant epilepsy and other inflammatory conditions. However, off-target stimulation can produce unwanted side effects that limit therapeutic stimulation and hinder the development of new neuromodulation therapies. Selective VNS (sVNS) offers a strategy to reduce off-target organ activation; however, this approach is not available in humans, and no implantable or portable devices exist to trial sVNS in the clinical setup. This work aimed to design, manufacture, and validate an implantable wireless, battery-free stimulator with a selectively addressable output stage for targeted current delivery to specific regions of the human vagus nerve.<i>Approach.</i>We developed a near-field communication-controlled, wirelessly powered, battery-free, temporary implantable multichannel stimulation device, compatible with a 15-channel sVNS cuff electrode (14 selective electrode pairs and one circumferential whole-nerve channel). The device was encapsulated for short-term implantation and evaluated through benchtop characterisation, accelerated ageing, and validation in an acute porcine and a pilot human study.<i>Main result.</i>The sVNS device was evaluated in a porcine (<i>n</i>= 4) trial and a first-in-human pilot study (<i>n</i>= 1). Selective bradycardia of 23.28 ± 12.91% was observed in pigs and 7.5% in the human participant. In the human, a clear separation of bradycardic and tachycardic effects was observed, with additional selectivity in laryngeal activity. Cardiac and laryngeal responses were separated by 231° around the circumference of the nerve.<i>Significance</i>. This work demonstrates the feasibility of wireless battery-free sVNS for cardiac applications using a temporary implantable device. Geometrically selective stimulation has the potential to improve therapeutic efficacy while reducing stimulation-related side effects, and may facilitate future therapies for heart failure and other autonomic disorders.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}