Journal of neural engineering最新文献

{"title":"Decomposition of retinal ganglion cell electrical images for cell type and functional inference.","authors":"Eric G Wu, Andra M Rudzite, Martin O Bohlen, Peter H Li, Alexandra Kling, Sam Cooler, Colleen Rhoades, Nora Brackbill, Alex R Gogliettino, Nishal P Shah, Sasidhar S Madugula, Alexander Sher, Alan M Litke, Greg D Field, E J Chichilnisky","doi":"10.1088/1741-2552/ade344","DOIUrl":"10.1088/1741-2552/ade344","url":null,"abstract":"<p><p><i>Objective.</i>Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.<i>Approach.</i>The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose EI into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.<i>Main results.</i>The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.<i>Significance.</i>These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268269","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":"Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.","authors":"Victoria Peterson, Valeria Spagnolo, Catalina M Galván, Nicolás Nieto, Rubén D Spies, Diego H Milone","doi":"10.1088/1741-2552/addb7a","DOIUrl":"10.1088/1741-2552/addb7a","url":null,"abstract":"<p><p><i>Objective</i>. Controlling a motor imagery brain-computer interface (MI-BCI) can be challenging, requiring several sessions of practice. Electroencephalography (EEG)-based BCIs are particularly affected by cross-session variability. In this scenario, it is crucial to implement co-adaptive systems, where the machine adapts the decoding algorithm while the user learns how to control the BCI. To support the user learning process, it is essential to measure and provide real-time feedback on self-modulation skills. This study aims to develop a method for online assessment of MI modulation capability to build co-adaptive BCIs that improve both user performance and system accuracy.<i>Approach</i>. Backward optimal transport for domain adaptation allows across-session MI-BCI usage without classifier retraining. Using the cued label to guide the adaptation, a supportive backward adaptation (SBA) method is defined. The required model effort to perform a trial adaptation is proposed as an online metric of MI modulation skills. We conducted experiments on both real and simulated data to demonstrate that this metric effectively informs about the the discriminability and stability of the EEG patterns related to the MI task. The proposed metric is validated by means of Riemannian distinctiveness metrics.<i>Main Results</i>. Our findings show that the associated effort when applying SBA provides a meaningful way of evaluating EEG patterns discriminability, being significantly correlated with Riemannian distinctiveness metrics.<i>Significance</i>. This study introduces a novel framework for co-adaptive BCI learning that performs data adaptation while assessing the MI-BCI skills of the user. The proposed SBA approach can enhance BCI performance by facilitating session-to-session adaptation and empowering users with valuable feedback based on their current MI modulation strategy. This framework represents a significant advancement in developing user-centered, co-adaptive MI-BCIs that effectively support and enhance user capabilities.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121787","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":"Assessing changes in whole-brain structural connectivity in the unilateral 6-hydroxydopamine rat model of Parkinson's disease using diffusion imaging and tractography.","authors":"Mikhail Moshchin, Roger J Schultz, Kevin P Cheng, Susan Osting, James Koeper, Matthew Laluzerne, James K Trevathan, Andrea Brzeczkowski, Cuong P Luu, John-Paul J Yu, Richard F Betzel, Wendell B Lake, Samuel A Hurley, Kip A Ludwig, Aaron J Suminski","doi":"10.1088/1741-2552/ade567","DOIUrl":"10.1088/1741-2552/ade567","url":null,"abstract":"<p><p><i>Objective.</i>Parkinson's disease (PD) is a multifactorial, progressive neurodegenerative disease that has a profound impact on those it afflicts. Its hallmark pathophysiology is characterized by degeneration of dopaminergic (DA) neurons in the midbrain which trigger a host of motor and non-motor symptoms. Many preclinical research efforts utilize unilateral lesion models to assess the neural mechanisms of PD and explore new therapeutic approaches because these models produce similar motor symptoms to those of PD patients. The goal of this work is to examine changes in brain structure resulting from a unilateral lesion both within the nigrostriatal system, where DA neurons are lost, and throughout the brain.<i>Methods.</i>Using multi-shell diffusion magnetic resonance imaging and correlational tractography, we assessed microstructural changes throughout the brain resulting from unilateral injection of 6-hydroxydopamine in the median forebrain bundle.<i>Resutls.</i>Following lesioning, the PD phenotype was confirmed using behavioral and histological assessment. Correlational tractography found networks of fiber tracts that were either positively or negatively correlated with lesion status throughout the brain. Analyzing patterns of intra- and inter-hemispheric connectivity between the positively and negatively correlated fiber tracts revealed two separate neural networks. The first contained only negatively correlated fibers in the lesioned hemisphere consistent with the local effects of the lesion (i.e. DA depletion in the nigrostriatal system). The second contained systematically overlapping fiber tracts in the lesioned and non-lesioned hemispheres including the olfactory system and cerebellum, which we suggest are indicative of adaptive mechanisms to compensate for the lesion.<i>Conclusion.</i>Taken together, these results suggest that correlational tractography is a reasonable tool to examine whole brain structural changes in rodent models of neurodegenerative disease, and may have future translational value as a diagnostic tool for patients with PD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318988","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":"Encoding algorithms for somatotopic restoration of somatic sensations in the upper-limb: a systematic review.","authors":"Alessia Scarpelli, Francesca Cordella, Loredana Zollo","doi":"10.1088/1741-2552/ade503","DOIUrl":"10.1088/1741-2552/ade503","url":null,"abstract":"<p><p><i>Objective.</i>Sensory feedback restoration represents a fundamental need for upper limb prosthesis users because it permits to feel somatic sensations during interactions with the environment. Considering the artificial sensory transduction, neuroprotheses should take advantage of effective encoding algorithms, which have the essential role, in the sensory feedback process, of coding the intended perception to the individual with the amputation. This paper presents a literature systematic review of the encoding algorithms employed for somatotopically restoring somatic sensations in upper limb of individuals with the intact arm or with an amputation.<i>Approach.</i>The methodologies adopted for the development of the encoding algorithms were deeply analyzed to describe what is the current state of the art on this topic. Encoding algorithms validated in literature on upper limb were grouped into three main categories (<i>Function-based</i>,<i>Bio-inspired</i>and<i>Hybrid</i>) and then compared and described.<i>Main results.</i><i>Function-based Algorithms</i>provide the user with high sensitivity, whereas if the verisimilitude to natural sensation and complexity are the most desirable features for sensory feedback, a<i>Bio-inspired</i>strategy would be the most suitable to implement. However,<i>Hybrid</i>solutions both evoked realistic sensations and enhanced discrimination capabilities.<i>Significance.</i>The conducted analysis represents a guide for understanding which type of encoding to choose, making a compromise between the characteristics of the elicited sensations and the achieved performance. This critical analysis will give the reader important information for understanding the potentiality of the encoding strategies to elicit different sensations for a specific application and for developing novel sensory restoration approaches.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311118","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":"Patch clamp recordings of action potentials from pyramidal neuron in hippocampus CA1 under focused ultrasound neurostimulation with MEMS self-focusing acoustic transducer.","authors":"Jaehoon Lee, Yongkui Tang, Akash Roy, Kianoush Sadeghian Esfahani, Su-Youne Chang, Eun S Kim","doi":"10.1088/1741-2552/ade7ae","DOIUrl":"10.1088/1741-2552/ade7ae","url":null,"abstract":"<p><p><i>Objective.</i>This study aims to investigate the modulatory effects of focused ultrasound (FUS) on neuronal activity at the single-cell level, using whole-cell patch clamp recordings in hippocampal slices.<i>Approach.</i>A self-focused acoustic transducer (SFAT) was designed and fabricated on a 127<i>µ</i>m-thick translucent lead zirconate titanate substrate to allow infrared light transmission for visualizing neurons during patch clamp experiments. The SFAT operates at 18.4 MHz, producing low-intensity FUS with a 46<i>µ</i>m focal diameter at a depth of 400<i>µ</i>m. Three types of SFAT-active, FUS-blocking control, and low-electromagnetic interference (EMI) versions-were developed to assess the effects of acoustic stimulation, thermal heating, and EMI. Neuronal responses were recorded across 78 tissue samples from 29 animals using 48 combinations of acoustic parameters, including peak-to-peak voltage, pulse repetition frequency (PRF), and pulse duration.<i>Main results.</i>Whole-cell patch clamp recordings from CA1 pyramidal neurons in rat hippocampal slices revealed that FUS induces both inhibitory and excitatory effects on action potential firing, depending on the stimulation parameters. Inhibition was found to be the dominant response, while excitation was mainly attributable to thermal effects. Optimal inhibition was achieved with 60 Vpp (ISAPA = 2.11 W cm^-2), 35 kCycles/pulse (1.90 ms), and 100 Hz PRF, yielding a 60% success rate. Conversely, excitation was observed in 60% of trials using 120 Vpp (ISAPA = 8.44 W cm^-2), 50 kCycles/pulse (2.72 ms), and 20 Hz PRF.<i>Significance.</i>This work presents a novel neuromodulation platform that combines high-frequency focused ultrasound with real-time whole-cell patch clamp recording at single-neuron resolution. The results provide direct electrophysiological evidence of parameter-dependent, bidirectional modulation of neuronal activity by FUS, offering new insights into its underlying mechanisms and helping define stimulation protocols for future neurotherapeutic applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning on high-density EEG during a cognitive task distinguishes patients with Parkinson's disease from healthy controls.","authors":"Yves Denoyer, Joan Duprez, Jean-François Houvenaghel, Fabrice Wendling, Pascal Benquet","doi":"10.1088/1741-2552/ade6a9","DOIUrl":"10.1088/1741-2552/ade6a9","url":null,"abstract":"<p><p><i>Objective.</i>Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment. Its diagnosis, which used to be based on clinical assessment, increasingly relies on biomarkers. While electroencephalography (EEG) biomarkers are still at an experimental stage, they have been studied using deep learning (DL) models. Our aim was to determine whether a cognitive task could improve the accuracy of EEG-based disease detection by activating cortical regions affected by the disease.<i>Approach</i>. We trained a DL model to discriminate PD patients from controls based on their high-density EEG recordings. Previous studies have employed a range of preprocessing techniques, models and, predominantly, resting state (RS) EEG. We also investigated different network architectures and hyperparameters, and the role of spatial and temporal resolution.<i>Main results</i>. The best model gave a classification accuracy of 83% on the cognitive task EEG dataset and 76% on the RS EEG dataset. Sensitivity analysis indicated that the model predominantly uses specific temporal and spatial components of the EEG in the cognitive task condition, differing from the RS.<i>Significance</i>. Our results suggest that cortical activation by the cognitive task unveils EEG features that are effective in distinguishing between PD and controls. These features can be used by the model, thereby improving its diagnostic accuracy.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337386","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":"Annotating neurophysiologic data at scale with optimized human input.","authors":"Zhongchuan Xu, Brittany H Scheid, Erin C Conrad, Kathryn A Davis, Taneeta Ganguly, Michael A Gelfand, James J Gugger, Xiangyu Jiang, Joshua J LaRocque, William K S Ojemann, Saurabh R Sinha, Genna J Waldman, Joost Wagenaar, Nishant Sinha, Brian Litt","doi":"10.1088/1741-2552/ade402","DOIUrl":"10.1088/1741-2552/ade402","url":null,"abstract":"<p><p><i>Objective.</i>Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, particularly in annotation. While expert annotation remains the gold standard, it is time consuming to obtain and often poorly reproducible. Although automated annotation approaches exist, they rely on labeled data first to train machine learning algorithms, which limits their scalability. A semi-automated annotation approach that integrates human expertise while optimizing efficiency at scale is critically needed. To address this, we present Annotation Co-pilot, a human-in-the-loop solution that leverages deep active learning (AL) and self-supervised learning (SSL) to improve intracranial EEG (iEEG) annotation, significantly reducing the amount of human annotations.<i>Approach.</i>We automatically annotated iEEG recordings from 28 humans and 4 dogs with epilepsy implanted with two neurodevices that telemetered data to the cloud for analysis. We processed 1500 h of unlabeled iEEG recordings to train a deep neural network using a SSL method Swapping Assignments between View to generate robust, dataset-specific feature embeddings for the purpose of seizure detection. AL was used to select only the most informative data epochs for expert review. We benchmarked this strategy against standard methods.<i>Main result.</i>Over 80 000 iEEG clips, totaling 1176 h of recordings were analyzed. The algorithm matched the best published seizure detectors on two datasets (NeuroVista and NeuroPace responsive neurostimulation) but required, on average, only 1/6 of the human annotations to achieve similar accuracy (area under the ROC curve of 0.9628 ± 0.015) and demonstrated better consistency than human annotators (Cohen's Kappa of 0.95 ± 0.04).<i>Significance</i>. 'Annotation Co-pilot' demonstrated expert-level performance, robustness, and generalizability across two disparate iEEG datasets while reducing annotation time by an average of 83%. This method holds great promise for accelerating basic and translational research in electrophysiology, and potentially accelerating the pathway to clinical translation for AI-based algorithms and devices.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12223546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography.","authors":"Harshavardhana T Gowda, Zachary D McNaughton, Lee M Miller","doi":"10.1088/1741-2552/ade7af","DOIUrl":"10.1088/1741-2552/ade7af","url":null,"abstract":"<p><p><i>Objective.</i>In this article, we present data and methods for decoding speech articulations using surface electromyogram (EMG) signals. EMG-based speech neuroprostheses offer a promising approach for restoring audible speech in individuals who have lost the ability to speak intelligibly due to laryngectomy, neuromuscular diseases, stroke, or trauma-induced damage (e.g. from radiotherapy) to the speech articulators.<i>Approach.</i>To achieve this, we collect EMG signals from the face, jaw, and neck as subjects articulate speech, and we perform EMG-to-speech translation.<i>Main results.</i>Our findings reveal that the manifold of symmetric positive definite matrices serves as a natural embedding space for EMG signals. Specifically, we provide an algebraic interpretation of the manifold-valued EMG data using linear transformations, and we analyze and quantify distribution shifts in EMG signals across individuals.<i>Significance.</i>Overall, our approach demonstrates significant potential for developing neural networks that are both data- and parameter-efficient-an important consideration for EMG-based systems, which face challenges in large-scale data collection and operate under limited computational resources on embedded devices.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487574","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 robust neural prosthetic control strategy against arm position variability and fatigue based on multi-sensor fusion.","authors":"Shang Shi, Jianjun Meng, Zongtian Yin, Weichao Guo, Xiangyang Zhu","doi":"10.1088/1741-2552/ade504","DOIUrl":"10.1088/1741-2552/ade504","url":null,"abstract":"<p><p><i>Objective</i>. Multi-modal sensor fusion comprising surface electromyography (sEMG) and A-mode ultrasound (US) has yielded satisfactory performance in gesture recognition, aiding amputees in restoring upper limb function. However, prior research conducted in laboratory settings with consistent arm positions lacks practical application for amputees using prostheses. Additionally, motion tests utilized in current studies necessitate prolonged gesture execution, while constant muscle contractions introduce fatigue and increase misclassification risk in practical applications. Consequently, implementing a robust control is imperative to mitigate the limitations of constant arm positions and muscle contractions.<i>Approach</i>. This paper introduces a novel decoding strategy for online applications based on A-mode US, sEMG, and inertial movement unit (IMU) sensor fusion. The decoding process comprises four stages: arm position selection, sEMG threshold, pattern recognition, and a post-processing strategy, which preserves the previous short-duration hand gesture during rest and aims to improve prosthetic hand control performance for practical applications.<i>Main results</i>. The offline classification accuracy achieves 96.02% based on fusion sensor decoding. It drops to 90.72% for healthy participants when wearing an arm fixture that simulates the load of a real prosthesis. The implementation of the post-processing strategy results in a 92.51% online classification accuracy (ONCA) for recognized gestures in three varied arm positions, significantly higher than the 78.97% ONCA achieved when the post-processing strategy is disabled.<i>Significance</i>. The post-processing strategy mitigates constant muscle contraction, demonstrating high robustness to prosthetic hand control. The proposed online decoding strategy achieves remarkable performance on customized prostheses for two amputees across various arm positions, providing a promising prospect for multi-modal sensor fusion based prosthetic applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311117","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":"Contralesional recruitment and localization of EEG signal complexity in stroke: a recurrence quantification analysis of hierarchical motor tasks.","authors":"Parikshat Sirpal, Nishaal Parmar, Hazem H Refai, Julius P A Dewald, Yuan Yang","doi":"10.1088/1741-2552/ade6ab","DOIUrl":"10.1088/1741-2552/ade6ab","url":null,"abstract":"<p><p><i>Objective.</i>Effective characterization of neural complexity during motor execution tasks enhances understanding of maladaptive cortical reorganization in stroke and inform targeted rehabilitation. While traditional EEG analyses often do not consider nonlinear temporal dynamics, we introduce a recurrence based computational framework to quantify cortical complexity during hierarchical motor tasks. Here, we evaluate contralesional motor system engagement in stroke survivors using recurrence quantification analysis (RQA), ensuring sensitivity to nonlinear and temporally structured cortical activity.<i>Approach</i>. RQA was applied to EEG signals recorded during shoulder abduction (SABD) at 20% and 40% torque levels to characterize nonlinear cortical dynamics and quantify complexity distinguishing adaptive from maladaptive motor system engagement. Spatially resolved recurrence metrics were compared between stroke and control participants to elucidate compensatory cortical reorganization linked to motor impairment and hierarchical task demands.<i>Results</i>. Our findings show a statistically significant increase in EEG signal complexity within the contralesional hemisphere of stroke participants, particularly under higher SABD loads. Consistent with previous studies, we observed abnormal muscle coactivation patterns between proximal and distal muscles, along with distinct shifts in EMG vector direction in stroke-impaired limbs. These shifts in coactivation patterns suggest constraints in muscle coactivation patterns resulting from losses in corticofugal projections and upregulated brainstem pathways.<i>Significance</i>. We introduce a novel application of RQA to quantify nonlinear EEG complexity during motor execution in chronic stroke. Our results show that increased EEG complexity reflects greater recruitment of contralesional motor pathways, indicating maladaptive cortical reorganization linked to impaired motor control. Unlike traditional spectral or connectivity-based EEG signal processing methods, RQA quantifies temporally evolving, nonlinear recurrence dynamics, serving as a marker of maladaptive contralesional motor recruitment, positioning RQA as a promising, clinically meaningful, and computationally efficient tool to evaluate cortical dynamics and guide targeted neurorehabilitation strategies aimed at minimizing maladaptive plasticity.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}