Journal of neural engineering最新文献

{"title":"Transcranial focused ultrasound remotely modulates extrastriate visual cortex by stimulating frontal eye field with subregion specificity.","authors":"Kai Yu, Samantha Schmitt, Yunruo Ni, Emily Crane, Matthew A Smith, Bin He","doi":"10.1088/1741-2552/ad9406","DOIUrl":"10.1088/1741-2552/ad9406","url":null,"abstract":"<p><strong>Objective: </strong>Low-intensity transcranial focused ultrasound (tFUS) has emerged as a powerful neuromodulation tool characterized by its deep penetration and precise spatial targeting to influence neural activity. Our study directed low-intensity tFUS stimulation onto a region of prefrontal cortex (the frontal eye field, or FEF) of a rhesus macaque to examine its impact on a remote site, the extrastriate visual cortex (area V4) through this top-down modulatory circuit that has been studied extensively with electrical microstimulation.</p><p><strong>Approach: </strong>To measure the impact of tFUS stimulation, we recorded local field potentials (LFPs) and multi-unit spiking activities from a multi-electrode array implanted in the visual cortex. To deliver tFUS stimulation, we leveraged a customized 128-element random array ultrasound transducer with precise spatial targeting.</p><p><strong>Main results: </strong>We observed that tFUS stimulation in FEF produced modulation of V4 neuronal activity, either through enhancement or suppression, dependent on the pulse repetition frequency of the tFUS stimulation. Electronically steering the transcranial ultrasound focus through the targeted FEF cortical region produced changes in the level of modulation, indicating that the tFUS stimulation was spatially targeted within FEF. Modulation of V4 activity was confined to specific frequency bands, and this modulation was dependent on the presence or absence of a visual stimulus during tFUS stimulation. A control study targeting the insula produced no effect, emphasizing the region-specific nature of tFUS neuromodulation.</p><p><strong>Significance: </strong>Our findings shed light on the capacity of tFUS to modulate specific neural pathways and provide a comprehensive understanding of its potential applications for neuromodulation within brain networks.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670149","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 multi-feature fusion graph attention network for decoding motor imagery intention in spinal cord injury patients.","authors":"Jiancai Leng, Licai Gao, Xiuquan Jiang, Yitai Lou, Yuan Sun, Chen Wang, Jun Li, Heng Zhao, Feng Chao, Fangzhou Xu, Yang Zhang, Tzyy-Ping Jung","doi":"10.1088/1741-2552/ad9403","DOIUrl":"10.1088/1741-2552/ad9403","url":null,"abstract":"<p><p>Electroencephalogram (EEG) signals exhibit multi-domain features, and electrode distributions follow non-Euclidean topology. To fully resolve the EEG signals, this study proposes a Temporal-Frequency-Spatial multi-domain feature fusion Graph Attention Network (TFSGAT) for motor imagery (MI) intention recognition in spinal cord injury (SCI) patients. The proposed model uses phase-locked value (PLV) to extract spatial phase connectivity information between EEG channels and continuous wavelet transform to extract valid EEG information in the time-frequency domain. It then models a graph data structure containing multi-domain information. The gated recurrent unit and GAT learn EEG's dynamic temporal-spatial information. Finally, the fully connected layer outputs the MI intention recognition results. After 10 times 10-fold cross-validation, the proposed model can achieve an average accuracy of 95.82%. Furthermore, this study analyzes the Event-Related Desynchronization/Event-Related Synchronization and PLV brain network to explore the brain activity of SCI patients during MI. This study confirms the potential of the proposed model in terms of EEG decoding performance and provides a reference for the mechanism of neural activity in SCI patients.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670006","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":"Decoding sensorimotor information from somatosensory cortex by flexible epicortical μECoG arrays in unrestrained behaving rats.","authors":"Deniz Kılınç Bülbül, Steven T Walston, Fikret Taygun Duvan, Jose A Garrido, Burak Guclu","doi":"10.1088/1741-2552/ad9405","DOIUrl":"10.1088/1741-2552/ad9405","url":null,"abstract":"<p><strong>Objective: </strong>Brain-computer interfaces (BCI) are promising for severe neurological conditions and there are ongoing efforts to develop state-of-the-art neural interfaces, hardware, and software tools. We tested the potential of novel reduced graphene oxide (rGO) electrodes implanted epidurally over the hind limb representation of the primary somatosensory (S1) cortex of rats and compared them to commercial platinum-iridium (Pt-Ir) 16-channel electrodes (active site diameter: 25 μm).</p><p><strong>Approach: </strong>Motor and somatosensory information was decoded offline from microelectrocorticography (μECoG) signals recorded while unrestrained rats performed a simple behavioral task: pressing a lever and the subsequent vibrotactile stimulation of the glabrous skin at three displacement amplitude levels and at two sinusoidal frequencies. μECoG data were initially analyzed by standard time-frequency methods. Next, signal powers of oscillatory bands recorded from multiple electrode channels were used as features for sensorimotor classification by a machine learning algorithm.</p><p><strong>Main results: </strong>Both electrode types performed quite well and similar to each other for predicting the motor interval and the presence of the vibrotactile stimulus. Average accuracies were relatively lower for predicting 3-class vibrotactile frequency and 4-class amplitude level by both electrode types.</p><p><strong>Significance: </strong>Given some confounding factors during the free movement of rats, the results show that both sensory and motor information can be recorded reliably from the hind limb area of S1 cortex by using μECoG arrays. The chronic use of novel rGO electrodes was demonstrated successfully. The hind limb area may be convenient for the future evaluation of new tools in neurotechnology, especially those for bidirectional BCIs.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670145","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 ANN models cortical-subcortical interaction during post-stroke recovery of finger dexterity.","authors":"Ashraf Kadry, Deborah Solomonow-Avnon, Sumner L Norman, Jing Xu, Firas Mawase","doi":"10.1088/1741-2552/ad8961","DOIUrl":"10.1088/1741-2552/ad8961","url":null,"abstract":"<p><p><i>Objective.</i>Finger dexterity, and finger individuation in particular, is crucial for human movement, and disruptions due to brain injury can significantly impact quality of life. Understanding the neurological mechanisms responsible for recovery is vital for effective neurorehabilitation. This study explores the role of two key pathways in finger individuation: the corticospinal (CS) tract from the primary motor cortex and premotor areas, and the subcortical reticulospinal (RS) tract from the brainstem. We aimed to investigate how the cortical-reticular network reorganizes to aid recovery of finger dexterity following lesions in these areas.<i>Approach.</i>To provide a potential biologically plausible answer to this question, we developed an artificial neural network (ANN) to model the interaction between a premotor planning layer, a cortical layer with excitatory and inhibitory CS outputs, and RS outputs controlling finger movements. The ANN was trained to simulate normal finger individuation and strength. A simulated stroke was then applied to the CS area, RS area, or both, and the recovery of finger dexterity was analyzed.<i>Main results.</i>In the intact model, the ANN demonstrated a near-linear relationship between the forces of instructed and uninstructed fingers, resembling human individuation patterns. Post-stroke simulations revealed that lesions in both CS and RS regions led to increased unintended force in uninstructed fingers, immediate weakening of instructed fingers, improved control during early recovery, and increased neural plasticity. Lesions in the CS region alone significantly impaired individuation, while RS lesions affected strength and to a lesser extent, individuation. The model also predicted the impact of stroke severity on finger individuation, highlighting the combined effects of CS and RS lesions.<i>Significance.</i>This model provides insights into the interactive role of cortical and subcortical regions in finger individuation. It suggests that recovery mechanisms involve reorganization of these networks, which may inform neurorehabilitation strategies.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484397","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-based spike sorting: a survey.","authors":"Luca M Meyer, Majid Zamani, János Rokai, Andreas Demosthenous","doi":"10.1088/1741-2552/ad8b6c","DOIUrl":"10.1088/1741-2552/ad8b6c","url":null,"abstract":"<p><p><i>Objective.</i>Deep learning is increasingly permeating neuroscience, leading to a rise in signal-processing applications for extracellular recordings. These signals capture the activity of small neuronal populations, necessitating 'spike sorting' to assign action potentials (spikes) to their underlying neurons. With the rise in publications delving into new methodologies and techniques for deep learning-based spike sorting, it is crucial to synthesise these findings critically. This survey provides an in-depth evaluation of the approaches, methodologies and outcomes presented in recent articles, shedding light on the current state-of-the-art.<i>Approach.</i>Twenty-four articles published until December 2023 on deep learning-based spike sorting have been examined. The proposed methods are divided into three sub-problems of spike sorting: spike detection, feature extraction and classification. Moreover, integrated systems, i.e. models that detect spikes and extract features or do classification within a single network, are included.<i>Main results.</i>Although most algorithms have been developed for single-channel recordings, models utilising multi-channel data have already shown promising results, with efficient hardware implementations running quantised models on application-specific integrated circuits and field programmable gate arrays. Convolutional neural networks have been used extensively for spike detection and classification as the data can be processed spatiotemporally while maintaining low-parameter models and increasing generalisation and efficiency. Autoencoders have been mainly utilised for dimensionality reduction, enabling subsequent clustering with standard methods. Also, integrated systems have shown great potential in solving the spike sorting problem from end to end.<i>Significance.</i>This survey explores recent articles on deep learning-based spike sorting and highlights the capabilities of deep neural networks in overcoming associated challenges, but also highlights potential biases of certain models. Serving as a resource for both newcomers and seasoned researchers in the field, this work provides insights into the latest advancements and may inspire future model development.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515454","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":"Functional connectivity of stimulus-evoked brain responses to natural speech in post-stroke aphasia.","authors":"Ramtin Mehraram, Pieter De Clercq, Jill Kries, Maaike Vandermosten, Tom Francart","doi":"10.1088/1741-2552/ad8ef9","DOIUrl":"10.1088/1741-2552/ad8ef9","url":null,"abstract":"<p><p><i>Objective</i>. One out of three stroke-patients develop language processing impairment known as aphasia. The need for ecological validity of the existing diagnostic tools motivates research on biomarkers, such as stimulus-evoked brain responses. With the aim of enhancing the physiological interpretation of the latter, we used EEG to investigate how functional brain network patterns associated with the neural response to natural speech are affected in persons with post-stroke chronic aphasia.<i>Approach</i>. EEG was recorded from 24 healthy controls and 40 persons with aphasia while they listened to a story. Stimulus-evoked brain responses at all scalp regions were measured as neural envelope tracking in the delta (0.5-4 Hz), theta (4-8 Hz) and low-gamma bands (30-49 Hz) using mutual information. Functional connectivity between neural-tracking signals was measured, and the Network-Based Statistics toolbox was used to: (1) assess the added value of the neural tracking vs EEG time series, (2) test between-group differences and (3) investigate any association with language performance in aphasia. Graph theory was also used to investigate topological alterations in aphasia.<i>Main results</i>. Functional connectivity was higher when assessed from neural tracking compared to EEG time series. Persons with aphasia showed weaker low-gamma-band left-hemispheric connectivity, and graph theory-based results showed a greater network segregation and higher region-specific node strength. Aphasia also exhibited a correlation between delta-band connectivity within the left pre-frontal region and language performance.<i>Significance.</i>We demonstrated the added value of combining brain connectomics with neural-tracking measurement when investigating natural speech processing in post-stroke aphasia. The higher sensitivity to language-related brain circuits of this approach favors its use as informative biomarker for the assessment of aphasia.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584028","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 leadless power transfer and wireless telemetry solutions for an endovascular electrocorticography.","authors":"Zhangyu Xu, Majid Khazaee, Nhan Duy Truong, Deniel Havenga, Armin Nikpour, Arman Ahnood, Omid Kavehei","doi":"10.1088/1741-2552/ad8dfe","DOIUrl":"10.1088/1741-2552/ad8dfe","url":null,"abstract":"<p><p><i>Objective</i>. Endovascular brain-computer interfaces (eBCIs) offer a minimally invasive way to connect the brain to external devices, merging neuroscience, engineering, and medical technology. Currently, solutions for endovascular electrocorticography (ECoG) include a stent in the brain with sensing electrodes, a chest implant to accommodate electronic components to provide power and data telemetry, and a long (tens of centimeters) cable travel through vessels with a set of wires in between. Removing this long cable is the key to the clinical viability of eBCIS as it carries risks and limitations, especially for patients with fragile vasculature.<i>Approach</i>. This work introduces a wireless and leadless telemetry and power transfer solution for ECoG. The proposed solution includes an optical telemetry module and a focused ultrasound (FUS) power transfer system. The proposed system can be miniaturised to fit in an endovascular stent, removing the need for long, intrusive cables.<i>Main results</i>. The optical telemetry achieves data transmission speeds of over 2 Mbit/s, capable of supporting 41 ECoG channels at a 2 kHz sampling rate with 24-bit resolution. The FUS power transfer system delivers up to 10 mW of power to the implant through the scalp(6 mm), skull(10 mm), and subdural space(5 mm), adhering to safety limits. Testing on bovine tissue (10 mm thick bone, 7 mm thick skin) confirmed the system's efficacy.<i>Significance</i>. This leadless and wireless solution eliminates the need for long cables and auxiliary implants, potentially reducing complications and enhancing the clinical applicability of eBCIs. The proposed system represents a step forward in enabling safer and more effective ECoG for a broader range of patients.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565398","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":"Anchoring temporal convolutional networks for epileptic seizure prediction.","authors":"Songhui Rao, Miaomiao Liu, Yin Huang, Hongye Yang, Jiarui Liang, Jiayu Lu, Yan Niu, Bin Wang","doi":"10.1088/1741-2552/ad8bf3","DOIUrl":"10.1088/1741-2552/ad8bf3","url":null,"abstract":"<p><p><i>Objective</i>. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to be shorter than the onset of antiepileptic, thus failing to prevent seizures. However, longer epilepsy prediction faces the problem that as the preictal period lengthens, it increasingly resembles the interictal period, complicating differentiation.<i>Approach</i>. To address these issues, we employ the sample entropy method for feature extraction from electroencephalography (EEG) signals. Subsequently, we introduce the anchoring temporal convolutional networks (ATCN) model for longer-term, patient-specific epilepsy prediction. ATCN utilizes dilated causal convolutional networks to learn time-dependent features from previous data, capturing temporal causal correlations within and between samples. Additionally, the model also incorporates anchoring data to enhance the performance of epilepsy prediction further. Finally, we proposed a multilayer sliding window prediction algorithm for seizure alarms.<i>Main results</i>. Evaluation on the Freiburg intracranial EEG dataset shows our approach achieves 100% sensitivity, a false prediction rate (FPR) of 0.09 per hour, and an average prediction time (APT) of 98.92 min. Using the CHB-MIT scalp EEG dataset, we achieve 97.44% sensitivity, a FPR of 0.12 per hour, and an APT of 93.54 min.<i>Significance</i>. These results demonstrate that our approach is adequate for seizure prediction over a more extended prediction range on intracranial and scalp EEG datasets. The APT of our approach exceeds the typical onset time of antiepileptic. This approach is particularly beneficial for patients who need to take medication at regular intervals, as they may only need to take their medication when our method issues an alarm. This capability has the potential to prevent seizures, which will greatly improve patients' quality of life.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142523980","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":"SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands.","authors":"Rongkai Liu, Quanjun Song, Tingting Ma, Hongqing Pan, Hao Li, Xinyan Zhao","doi":"10.1088/1741-2552/ad8b6e","DOIUrl":"10.1088/1741-2552/ad8b6e","url":null,"abstract":"<p><p><i>Objective.</i>Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.<i>Approach.</i>We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.<i>Main results.</i>The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.<i>Significance.</i>The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142515456","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":"Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.","authors":"Seungbin Park, Megan Lipton, Maria C Dadarlat","doi":"10.1088/1741-2552/ad83c0","DOIUrl":"https://doi.org/10.1088/1741-2552/ad83c0","url":null,"abstract":"<p><p><i>Objective.</i>Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from neural injury and disease. A critical step in implementing a BMI is to decode movement intention from recorded neural activity patterns in sensorimotor areas. Optical imaging, including two-photon (2p) calcium imaging, is an attractive approach for recording large-scale neural activity with high spatial resolution using a minimally-invasive technique. However, relating slow two-photon calcium imaging data to fast behaviors is challenging due to the relatively low optical imaging sampling rates. Nevertheless, neural activity recorded with 2p calcium imaging has been used to decode information about stereotyped single-limb movements and to control BMIs. Here, we expand upon prior work by applying deep learning to decode multi-limb movements of running mice from 2p calcium imaging data.<i>Approach.</i>We developed a recurrent encoder-decoder network (LSTM-encdec) in which the output is longer than the input.<i>Main results.</i>LSTM-encdec could accurately decode information about all four limbs (contralateral and ipsilateral front and hind limbs) from calcium imaging data recorded in a single cortical hemisphere.<i>Significance.</i>Our approach provides interpretability measures to validate decoding accuracy and expands the utility of BMIs by establishing the groundwork for control of multiple limbs. Our work contributes to the advancement of neural decoding techniques and the development of next-generation optical BMIs.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"21 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142606940","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}