Journal of neural engineering最新文献

{"title":"Predicting EEG seizures using graded spiking neural networks.","authors":"Yazin Al Musafir, Mostefa Mesbah","doi":"10.1088/1741-2552/adb455","DOIUrl":"10.1088/1741-2552/adb455","url":null,"abstract":"<p><p><i>Objective.</i>To develop and evaluate a novel, non-patient-specific epileptic seizure prediction system using graded spiking neural networks (GSNNs) implemented on Intel's Loihi 2 neuromorphic processor, addressing the challenges of real-time, energy-efficient prediction to improve patient quality of life.<i>Approach.</i>The GSNN-based system utilized the CHB-MIT dataset for training, integrating hyperparameter optimization, electroencephalogram (EEG) channel selection for data reduction, and a multi-windowed voting mechanism for robustness against noise and artifacts. The system was deployed on Intel's Loihi 2 processor, leveraging its neuromorphic architecture for improved computational efficiency.<i>Main results.</i>The proposed system achieved a non-patient-specific prediction accuracy of 99.14%, outperforming traditional seizure prediction methods. The implementation achieved a throughput of 21.6 EEG segment inputs per second with an energy consumption of 25.104 mJ per input. Additionally, GSNN demonstrated a 6.26 times improvement in event sparsity and a 3.80 times improvement in synaptic communication sparsity compared to artificial neural networks.<i>Significance.</i>This study introduces a robust and energy-efficient GSNN-based framework for epileptic seizure prediction, significantly improving the potential for real-time, wearable applications. By enhancing efficiency and reducing computational complexity, the proposed system demonstrates the substantial promise of GSNNs in advancing neuromorphic computing and addressing critical challenges in epilepsy management.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392834","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":"Deep learning models as learners for EEG-based functional brain networks<sup />.","authors":"Yuxuan Yang, Yanli Li","doi":"10.1088/1741-2552/adba8c","DOIUrl":"10.1088/1741-2552/adba8c","url":null,"abstract":"<p><p><i>Objective.</i>Functional brain network (FBN) methods are commonly integrated with deep learning (DL) models for EEG analysis. Typically, an FBN is constructed to extract features from EEG data, which are then fed into a DL model for further analysis. Beyond this two-step approach, there is potential to embed FBN construction directly within DL models as a feature extraction module, enabling the models to learn EEG representations end-to-end while incorporating insights from FBNs. However, a critical prerequisite is whether DL models can effectively learn the FBN construction process.<i>Approach.</i>To address this, we propose using DL models to learn FBN matrices derived from EEG data. The ability of DL models to accurately reproduce these matrices would validate their capacity to learn the FBN construction process. This approach is tested on two publicly available EEG datasets, utilizing seven DL models to learn four representative FBN matrices. Model performance is assessed through mean squared error (MSE), Pearson correlation coefficient (Corr), and concordance correlation coefficient (CCC) between predicted and actual matrices.<i>Main results.</i>The results show that DL models achieve low MSE and relatively high Corr and CCC values when learning the Coherence network. Visualizations of predicted and error matrices reveal that while DL models capture the general structure of all four FBNs, certain regions remain difficult to model accurately. Additionally, a paired<i>t</i>-test comparing global efficiency and nodal degree between predicted and actual networks indicates that most predicted networks significantly differ from the actual networks (p<0.05).<i>Significance.</i>These findings suggest that while DL models can learn the connectivity relationships of certain FBNs, they struggle to capture the intrinsic topological structures. This highlights the irreplaceability of traditional FBN methods in EEG analysis and underscores the need for hybrid strategies that combine FBN methods with DL models for a more comprehensive analysis.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517737","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":"Motor somatotopy impacts imagery strategy success in human intracortical brain-computer interfaces.","authors":"N G Kunigk, H R Schone, C Gontier, W Hockeimer, A F Tortolani, N G Hatsopoulos, J E Downey, S M Chase, M L Boninger, B D Dekleva, J L Collinger","doi":"10.1088/1741-2552/adb995","DOIUrl":"10.1088/1741-2552/adb995","url":null,"abstract":"<p><p><i>Objective:</i>The notion of a somatotopically organized motor cortex, with movements of different body parts being controlled by spatially distinct areas of cortex, is well known. However, recent studies have challenged this notion and suggested a more distributed representation of movement control. This shift in perspective has significant implications, particularly when considering the implantation location of electrode arrays for intracortical brain-computer interfaces (iBCIs). We sought to evaluate whether the location of neural recordings from the precentral gyrus, and thus the underlying somatotopy, has any impact on the imagery strategies that can enable successful iBCI control.<i>Approach:</i>Three individuals with a spinal cord injury were enrolled in an ongoing clinical trial of an iBCI. Participants had two intracortical microelectrode arrays implanted in the arm and/or hand areas of the precentral gyrus based on presurgical functional imaging. Neural data were recorded while participants attempted to perform movements of the hand, wrist, elbow, and shoulder.<i>Main results:</i>We found that electrode arrays that were located more medially recorded significantly more activity during attempted proximal arm movements (elbow, shoulder) than did lateral arrays, which captured more activity related to attempted distal arm movements (hand, wrist). We also evaluated the relative contribution from the two arrays implanted in each participant to decoding accuracy during calibration of an iBCI decoder for translation and grasping tasks. For both task types, imagery strategy (e.g. reaching vs wrist movements) had a significant impact on the relative contributions of each array to decoding. Overall, we found some evidence of broad tuning to arm and hand movements; however, there was a clear bias in the amount of information accessible about each movement type in spatially distinct areas of cortex.<i>Significance:</i>These results demonstrate that classical concepts of somatotopy can have real consequences for iBCI use, and highlight the importance of considering somatotopy when planning iBCI implantation.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495038","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":"Sparse knowledge sharing (SKS) for privacy-preserving domain incremental seizure detection.","authors":"Jiayu An, Ruimin Peng, Zhenbang Du, Heng Liu, Feng Hu, Kai Shu, Dongrui Wu","doi":"10.1088/1741-2552/adb998","DOIUrl":"10.1088/1741-2552/adb998","url":null,"abstract":"<p><p><i>Objective</i>. Epilepsy is a neurological disorder that affects millions of patients worldwide. Electroencephalogram-based seizure detection plays a crucial role in its timely diagnosis and effective monitoring. However, due to distribution shifts in patient data, existing seizure detection approaches are often patient-specific, which requires customized models for different patients. This paper considers privacy-preserving domain incremental learning (PP-DIL), where the model learns sequentially from each domain (patient) while only accessing the current domain data and previously trained models. This scenario has three main challenges: (1) catastrophic forgetting of previous domains, (2) privacy protection of previous domains, and (3) distribution shifts among domains.<i>Approach</i>. We propose a sparse knowledge sharing (SKS) approach. First, Euclidean alignment is employed to align data from different domains. Then, we propose an adaptive pruning approach for SKS to allocate subnet for each domain adaptively, allowing specific parameters to learn domain-specific knowledge while shared parameters to preserve knowledge from previous domains. Additionally, supervised contrastive learning is employed to enhance the model's ability to distinguish relevant features.<i>Main Results</i>. Experiments on two public seizure datasets demonstrated that SKS achieved superior performance in PP-DIL.<i>Significance</i>. SKS is a rehearsal-free privacy-preserving approach that effectively learns new domains while minimizing the impact on previously learned domains, achieving a better balance between plasticity and stability.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495039","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 0.53-<i>μ</i>W/channel calibration-free spike detection IC with 98.8-%-accuracy based on stationary wavelet transforms and Teager energy operators.","authors":"Zhining Zhou, Zichen Hu, Hongming Lyu","doi":"10.1088/1741-2552/adb5c4","DOIUrl":"10.1088/1741-2552/adb5c4","url":null,"abstract":"<p><p><i>Objective</i>. The brain-computer interface is currently experiencing a surge in the number of recording channels, resulting in a vast amount of raw data. It has become crucial to reliably detect neural spikes from a large population of neurons in the presence of noise, in order to constrain the transmission bandwidth.<i>Approach</i>. We investigate various time-frequency analysis methods for spike detection, followed by an exploration of energy operators amplifying spikes and signal statistics for adaptive thresholding. Subsequently, we introduce a precise and computationally efficient spike detection module, leveraging stationary wavelet transform (SWT), Teager energy operator, and root-mean-square calculator. This module is capable of autonomously adapting to different levels of noise. The SWT effectively eliminates high-frequency noise, enhancing the performance of the energy operators. The hardware computational process is simplified through the use of the lifting scheme and a channel-interleaving architecture.<i>Main results</i>. We evaluate the proposed spike detector with adaptive threshold on the publicly available WaveClus datasets. The detector achieves an average accuracy of 98.84%. The application-specific integrated circuit (ASIC) implementation results of the spike detector demonstrate an optimized interleaving channel of 8. In a 65 nm technology, the 8-channel spike detector consumes a power of 0.532<i>μ</i>W Ch^-1and occupies an area of 0.00645 mm²Ch^-1, operating at a 1.2 V supply voltage.<i>Significance</i>. The proposed spike detection processor offers one of the highest accuracies among state-of-the-art spike detection methods. Importantly, the ASIC explores the considerations in the scalability and hardware costs. The proposed design provides a systematic solution on spike detection with adaptive thresholding, offering a high accuracy while maintaining low power and area consumptions.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416582","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":"Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.","authors":"Stephanie Cernera, Tan Gemicioglu, Julia Berezutskaya, Richard Csaky, Maxime Verwoert, Daniel Polyakov, Sotirios Papadopoulos, Valeria Spagnolo, Juliana Gonzalez Astudillo, Satyam Kumar, Hussein Alawieh, Dion Kelly, Joanna R G Keough, Araz Minhas, Matthias Dold, Yiyuan Han, Alexander McClanahan, Mousa Mustafa, Juan Jose Gonzalez-Espana, Florencia Garro, Angela Vujic, Kriti Kacker, Christoph Kapeller, Simon Geukes, Ceci Verbaarschot, Michael Wimmer, Mushfika Sultana, Sara Ahmadi, Christian Herff, Andreea Ioana Sburlea, Camille Jeunet, David E Thompson, Marianna Semprini, Richard Andersen, Sergey Stavisky, Eli Kinney-Lang, Fabien Lotte, Jordy Thielen, Xing Chen, Victoria Peterson, Aysegul Gunduz, Theresa Vaughan, Davide Valeriani","doi":"10.1088/1741-2552/adb335","DOIUrl":"10.1088/1741-2552/adb335","url":null,"abstract":"<p><p>The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367155","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":"Label-free full-thickness imaging of porcine vagus nerve fascicular anatomy by polarization-sensitive optical coherence tomography.","authors":"Yong-Chul Yoon, Ilyas Saytashev, Rex Chin-Hao Chen, Megan Settell, Fernando Guastaldi, Daniel X Hammer, Kip A Ludwig, Benjamin J Vakoc","doi":"10.1088/1741-2552/adb5c3","DOIUrl":"10.1088/1741-2552/adb5c3","url":null,"abstract":"<p><p><i>Objective.</i>Improving the efficacy of vagus nerve (VN) stimulation therapy requires a detailed understanding of the anatomical and functional organization of nerve fiber bundles and their fascicles. Various<i>ex-vivo</i>imaging platforms have been optimized for this purpose. However, all existing tools with micrometer resolution require labeling to enhance the fascicle contrast, and this labeling is resource-intensive and time-consuming. Polarization-sensitive optical coherence tomography (PS-OCT) was previously used to perform high-speed, label-free small animal (rat) sciatic nerve imaging but has not been applied for imaging the full-thickness large animal VNs (>1 mm diameter thick) due to tissue-limited imaging depth. We developed a PS-OCT platform that circumvents this problem and demonstrate high-speed label-free imaging of full-depth, multiple centimeters-long mammalian VNs for the first time.<i>Approach.</i>We employed a custom-built PS-OCT system with a dual-surface scanning microscope to capture opposite sides of the sample in a single frame. A tailored post-processing algorithm maximized fascicle contrast and merged the two surfaces together. Multi-centimeter-long porcine VNs were imaged.<i>Main Results.</i>Our approach reconstructed fascicle information throughout the full-thickness of the VN when compressed to a 650<i>μ</i>m thickness. Moreover, we cross-validated PS-OCT measurements of fascicular organization and retardance to assess myelination against pair histology from the same specimens, showing Spearman's rank correlation coefficient value of 0.69 (<i>p</i>-value < 0.001).<i>Significance.</i>We demonstrated a label-free optical imaging method for large-volume VN imaging. The time to image a 6.8 cm nerve was 680 s with 0.1 mm s^-1longitudinal sample translation speed, which is more than two orders of magnitude faster than existing modalities that require labeling. With this gain in speed and the possibility of label-free quantification of a fascicle's myelination level, important studies on inter-sample variability in fascicle organization become feasible.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416585","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":"Real-time control of a hearing instrument with EEG-based attention decoding.","authors":"Jens Hjortkjær, Daniel D E Wong, Alessandro Catania, Jonatan Märcher-Rørsted, Enea Ceolini, Søren A Fuglsang, Ilya Kiselev, Giovanni Di Liberto, Shih-Chii Liu, Torsten Dau, Malcolm Slaney, Alain de Cheveigné","doi":"10.1088/1741-2552/ad867c","DOIUrl":"https://doi.org/10.1088/1741-2552/ad867c","url":null,"abstract":"<p><p>Enhancing speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly, but hearing devices cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of neurosteering, in which a hearing instrument selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present and discuss a real-time brain-computer interface system that combines a stimulus-response model based on canonical correlation analysis for real-time EEG attention decoding, coupled with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. We provide an overview of the system and its various components, discuss prospects and limitations of the technology, and illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495040","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":"When neuromodulation met control theory.","authors":"Roberto Guidotti, Alessio Basti, Giulia Pieramico, Antea D'Andrea, Saeed Makkinayeri, Mauro Pettorruso, Timo Roine, Ulf Ziemann, Risto J Ilmoniemi, Gian Luca Romani, Vittorio Pizzella, Laura Marzetti","doi":"10.1088/1741-2552/ad9958","DOIUrl":"10.1088/1741-2552/ad9958","url":null,"abstract":"<p><p>The brain is a highly complex physical system made of assemblies of neurons that work together to accomplish elaborate tasks such as motor control, memory and perception. How these parts work together has been studied for decades by neuroscientists using neuroimaging, psychological manipulations, and neurostimulation. Neurostimulation has gained particular interest, given the possibility to perturb the brain and elicit a specific response. This response depends on different parameters such as the intensity, the location and the timing of the stimulation. However, most of the studies performed so far used previously established protocols without considering the ongoing brain activity and, thus, without adaptively targeting the stimulation. In control theory, this approach is called open-loop control, and it is always paired with a different form of control called closed-loop, in which the current activity of the brain is used to establish the next stimulation. Recently, neuroscientists are beginning to shift from classical fixed neuromodulation studies to closed-loop experiments. This new approach allows the control of brain activity based on responses to stimulation and thus to personalize individual treatment in clinical conditions. Here, we review this new approach by introducing control theory and focusing on how these aspects are applied in brain studies. We also present the different stimulation techniques and the control approaches used to steer the brain. Finally, we explore how the closed-loop framework will revolutionize the way the human brain can be studied, including a discussion on open questions and an outlook on future advances.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775915","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":"Intrathecal delivery of BDNF to the lumbar spinal cord modulates lumbar interneurons activity in a feline model of spinal cord injury.","authors":"J Paz Amaya, F Marchionne, A J Krupka, T Duong, M A Lemay","doi":"10.1088/1741-2552/adb0f3","DOIUrl":"10.1088/1741-2552/adb0f3","url":null,"abstract":"<p><p><i>Objective.</i>In the present study, we examined the correlations between the recovery of stepping obtained with intrathecal brain derived neurotrophic factor (BDNF) delivery to the lumbar spinal cord and the firing of the lumbar spinal interneurons in a feline model of spinal cord injury (SCI).<i>Approach. In-vivo</i>extracellular recordings of spinal neurons were conducted using two 64-channel microelectrode arrays inserted in the intermediate zone of the L3-L7 segments of cats spinalized at the T11-T12 level that received either saline or BDNF delivered intrathecally to the lumbar cisterna via an implanted minipump. Interneuronal activity was explored in terms of averaged neuronal firing properties and in terms of spike train interactions.<i>Main results.</i>With respect to averaged neuronal firing properties, we observed a significant increase in firing frequency in BNDF-treated animals and a similar distribution of the units' preferred phase of firing relative to the step cycle between the groups. With respect to spike train interactions, we observed higher synchrony of firing in BDNF-treated animals as well as less dependency on the unit's past firing.<i>Significance.</i>Studies conducted in feline models of complete SCI show a gradual recovery of hindlimb stepping after intensive treadmill training. Similarly, delivery of neurotrophins such as BDNF or neurotrophin-3 to the injury site via cellular transplant or via implantable mini-pump to the lumbar cisterna has been shown to promote recovery of locomotor behavior in the absence of locomotor training. The results from this study suggest that BDNF treatment sets the overall population in a state of high excitability, which along with higher synchrony and ensemble-dependent behavior, allows for the proper integration of cutaneous and proprioceptive input resulting in treadmill locomotor recovery after SCI.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076775","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}