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From Brain Signals to Recovery: Neural Sensing for Functional Restoration
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From Brain Signals to Recovery: Neural Sensing for Functional Restoration

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 10 September 2026 | Viewed by 1998

Editors

Dr. Yingchun Zhang

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Desai Sethi Urology Institute, Miami Project to Cure Paralysis, The Engineering for Precision Rehabilitation (EPR) Laboratory, University of Miami, Coral Gables, FL, USA
Interests: biomedical signal processing; BCI; multimodal neuroimaging; neural engineering; neuromodulation; precision rehabilitation
Dr. Su Liu

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Desai Sethi Urology Institute, Department of Neurology, Miami Project to Cure Paralysis, University of Miami, Coral Gables, FL 33136, USA
Interests: biomedical signal processing; clinical neural engineering; neuromodulation; BCI; cognitive neuroscience

Special Issue Information

Dear Colleagues, 

Brain–computer interfaces (BCIs), in a broad conceptual sense, are systems that acquire, interpret, and utilize brain signals to control external devices or provide neural feedback. These systems hold transformative potential for restoring sensorimotor function and enhancing the quality of life of individuals with neurological impairments. Among the most informative sensing modalities for BCI development are electroencephalography (EEG) and intracranial EEG (iEEG), which offer complementary insights into brain dynamics.

This Special Issue aims to highlight recent advances in neural sensing technologies, neuromodulation, signal processing, neural decoding, and machine learning approaches that leverage electrophysiological signatures for therapeutic and rehabilitative applications. We welcome original research and review articles that focus on the development, validation, and clinical integration of EEG and iEEG systems in neurorehabilitation and neural plasticity—particularly in contexts such as stroke, epilepsy, spinal cord injury, and other neuromotor or neurological disorders. The scope of this Special Issue includes sensor design, neurobiomarker discovery, multimodal data integration, real-time BCI systems, neuromodulation strategies, and translational neuroscience.

This Special Issue fits within the scope of Sensors by emphasizing the design, implementation, and application of biosensing technologies in healthcare, with a particular focus on neural sensing for therapeutic/rehabilitation and assistive systems.

Dr. Yingchun Zhang
Dr. Su Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • EEG
  • iEEG
  • EMG
  • brain–computer interface (BCI)
  • neurorehabilitation
  • neural plasticity
  • neuromodulation
  • biosensors
  • motor decoding

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Published Papers (3 papers)

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Research

37 pages, 4074 KB  
Article
Multimodal EEG and Eye-Tracking Assessment of Perceived Mental Workload and Situation Awareness in a Visual Display Terminal Simulation Task
by Mingchong Xiao, Zhengwen Zhou, Han Peng, Zhe Lin, Yibo Yang, Jialong Chen and Liangjie Guo
Sensors 2026, 26(15), 4835; https://doi.org/10.3390/s26154835 - 31 Jul 2026
Viewed by 355
Abstract
Visual display terminal (VDT) tasks are common in safety-critical systems, where operators must continuously monitor dynamic information and maintain situation awareness (SA). This study examined whether EEG and eye-tracking technologies could support the assessment of SA and perceived mental workload in VDT tasks. [...] Read more.
Visual display terminal (VDT) tasks are common in safety-critical systems, where operators must continuously monitor dynamic information and maintain situation awareness (SA). This study examined whether EEG and eye-tracking technologies could support the assessment of SA and perceived mental workload in VDT tasks. Short-term incentive conditions were included only as an exploratory contextual factor. Thirty participants completed a SATEST-based VDT simulation task while subjective scales, behavioral performance, EEG signals, and eye-tracking data were collected during the perception, comprehension, and projection stages of SA. Physiological indicators associated with SA differences and workload-related patterns were examined using SART-based and NASA-TLX-based grouping, supplemented by continuous-score Spearman correlation and exploratory linear regression analyses. The incentive analysis was exploratory, underpowered for small-to-medium effects, and was not designed to validate an incentive-based cognitive-state regulation strategy. Saccade-related eye-tracking indicators differed between relatively higher-SA and lower-SA groups across several stages (p = 0.0043 to 0.0457, Cohen’s d = −0.670 to −1.002), while fronto-central relative theta power showed an SA-related difference during the SA1 perception stage (p = 0.0315, Cohen’s d = −0.746). Relatively higher perceived workload was associated with lower SART scores (p = 0.0187), lower SA2 accuracy (p = 0.0175), larger SA3 angular error (p = 0.0151), and lower frontal theta-band and alpha-band absolute power across selected stages (p = 0.0094 to 0.0294). The exploratory incentive analysis showed only a limited stage-specific difference in SA1 distance error, with no reliable differences in overall SART score, SA2 accuracy, SA3 angular error, or NASA-TLX score. Thus, the findings provide insufficient evidence that the present short-term incentive manipulation produced reliable cognitive-state regulation. Overall, these findings provide preliminary support for multimodal physiological assessment in VDT tasks and suggest that the identified physiological measures should be regarded as candidate indicators requiring further validation. Full article
(This article belongs to the Special Issue From Brain Signals to Recovery: Neural Sensing for Functional Restoration)
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16 pages, 2919 KB  
Article
Spatiotemporal Dual-Channel Interpretable Hybrid Neural Network for HD-sEMG-Based Gesture Recognition
by Zhefei Cai, Su Liu, Xinyue Li, Michael Houston, Yingle Fan and Yingchun Zhang
Sensors 2026, 26(14), 4602; https://doi.org/10.3390/s26144602 - 20 Jul 2026
Viewed by 420
Abstract
Accurate gesture recognition is crucial for precision control of upper limb prostheses. High-density surface electromyography (HD-sEMG) enhances spatial resolution and information richness of human gesture representation, thus improving myoelectric control of bionic limbs. Recently, deep learning has been increasingly applied to HD-sEMG to [...] Read more.
Accurate gesture recognition is crucial for precision control of upper limb prostheses. High-density surface electromyography (HD-sEMG) enhances spatial resolution and information richness of human gesture representation, thus improving myoelectric control of bionic limbs. Recently, deep learning has been increasingly applied to HD-sEMG to enhance gesture recognition performance. However, the black-box nature of neural networks limits their interpretability and model optimization, hindering their practical application. In this paper, we developed a spatiotemporal dual-channel interpretable hybrid neural network (STDC-Net), and validated it using the Capgmyo DB-a dataset. STDC-Net uses Feature Channels and Spatial Channels to process the feature and spatial information of sEMG signals respectively for increased interpretability. SHapley Additive exPlanations (SHAP) values are used to rank the feature importance, aiding feature filtering and reducing the impact of irrelevant features. Graph attention layers are used to calculate the connections between each Spatial Channel, illustrating the relationships between channels. Our results demonstrated the superior performance of our STDC-Net compared to state-of-the-art (SOTA) methods. STDC-Net achieved 99.8% accuracy with a 150 ms sliding window, exceeding real-time implementation requirements for intra-subject tasks. It reached an accuracy of 97.33 ± 2.53% after fine-tuning for inter-subject tasks, outperforming the SOTA methods. Importantly, the SHAP value maps and the channel connection maps enhance the interpretability of the neural networks by offering detailed insights into the contribution of input features and parameter interactions of the network. These findings suggest STDC-Net holds significant promise for real-time prosthetic control. Full article
(This article belongs to the Special Issue From Brain Signals to Recovery: Neural Sensing for Functional Restoration)
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17 pages, 385 KB  
Article
Assessing the Resilience of sEMG Classifiers to Sensor Malfunction and Signal Saturation
by Congyi Zhang, Dalin Zhou, Yinfeng Fang, Dongxu Gao and Zhaojie Ju
Sensors 2026, 26(8), 2386; https://doi.org/10.3390/s26082386 - 13 Apr 2026
Viewed by 737
Abstract
Surface electromyography (sEMG) is widely used for gesture recognition, yet the way classic feature–classifier pipelines fail under realistic signal degradations is still poorly quantified. Existing studies typically report accuracy on clean laboratory data, leaving open how amplitude saturation and channel dropout jointly affect [...] Read more.
Surface electromyography (sEMG) is widely used for gesture recognition, yet the way classic feature–classifier pipelines fail under realistic signal degradations is still poorly quantified. Existing studies typically report accuracy on clean laboratory data, leaving open how amplitude saturation and channel dropout jointly affect different feature combinations, classifiers, and subjects. In this work, we provide, to our knowledge, the first systematic robustness map of a conventional sEMG pipeline under controlledclipping and single-sensor failure. sEMG from nine subjects performing a multi-session, multi-gesture protocol is windowed (250 ms, 50 ms hop) and represented using four common time-domain features (Root Mean Square, Variance, Zero Crossing, and Waveform Length). We exhaustively evaluated single features and all pairwise fusions with three standard classifiers (Support Vector Machine (RBF kernel), Linear Discriminant Analysis, and Random Forest) over (i) a sweep of symmetric saturation thresholds (10−6–10−1) and (ii) five single-channel dropout scenarios, reporting subject-wise dispersion rather than aggregate scores alone. This design enables explicit characterization of the following: (1) accuracy recovery as clipping weakens for each feature pair; (2) dependency of robustness on which channel fails; and (3) differences among Support Vector Machine, Linear Discriminant Analysis, and Random Forest under identical degradations. The results show that lightweight feature pairs (Root Mean Square + Waveform Length, Variance + Zero Crossing, and Waveform Length + Zero Crossing) coupled with Random Forest form a consistently robust operating point, with performance recovering as clipping weakens and remaining resilient under single-channel dropout. Beyond robustness, the conventional pipeline trains substantially faster than representative deep learning baselines under a unified end-to-end timing definition, supporting real-time recalibration and repeated robustness sweeps in wearable deployments. Full article
(This article belongs to the Special Issue From Brain Signals to Recovery: Neural Sensing for Functional Restoration)
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