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Electroencephalogram/Electromyogram-Based Sensing Technologies for Biomedical Applications: Challenges and Possible Applications
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Electroencephalogram/Electromyogram-Based Sensing Technologies for Biomedical Applications: Challenges and Possible Applications

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2234

Special Issue Editors

Dr. Yang Zheng

E-Mail Website
Guest Editor
Institute of Engineering and Medicine Interdisciplinary Studies and the State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
Interests: EEG; EMG; neural–machine interface technology; neural activity detection
Dr. Hui Liu

E-Mail Website
Guest Editor
Cognitive Systems Laboratory, Faculty of Mathematic/Informatics, University of Bremen, 28359 Bremen, Germany
Interests: biosignal processing; feature selection and feature space reduction; human activity recognition; real-time recognition systems; knee bandage; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Xie

E-Mail Website
Guest Editor
Institute of Engineering and Medicine Interdisciplinary Studies and the State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
Interests: brain–computer interfaces and neurorehabilitation applications
Dr. Lili Li

E-Mail Website
Guest Editor
College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
Interests: EEG; eye tracking; disease recognition
Dr. Min Li

E-Mail Website
Guest Editor
Institute of Engineering and Medicine Interdisciplinary Studies and the State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China
Interests: robots for stroke rehabilitation; brain–computer interface; haptic feedback
Special Issues, Collections and Topics in MDPI journals
Dr. João Rodrigues

E-Mail Website
Guest Editor
Technology Engineering Group, International Iberian Nanotechnology Laboratory
Interests: biosignal processing; applied machine learning

Special Issue Information

Dear Colleagues,

As two of the most common electrogram techniques, electroencephalogram (EEG) and electromyogram (EMG), that record the electrical activity of cortical neurons and muscle fibers, respectively, have been in use for about 100 years. Despite their longevity, EEG/EMG technology remains active in research across multiple areas, including medical diagnosis, neural–machine interface, physical/cognitive function assessment, and rehabilitation. In particular, the recent emergence of new materials, fabrication techniques, and information processing methods has revitalized the application of EEG/EMG technology in many fields. The development of flexible sensors has made it possible for devices to be wearable like clothing. The miniaturization, lightweighting, and integration of signal acquisition devices have led to the development of multimodal and high-density signal acquisition. The massive increase in information has also posed a huge challenge to signal processing techniques, and artificial intelligence and deep learning have shown great potential in this area, driving the application of EEG/EMG technology in various new fields.

Therefore, this Special Issue aims to collate original research and review articles focused on recent advances, technologies, solutions, applications, and new challenges in EEG/EMG-based sensing technologies for biomedical applications.

Dr. Yang Zheng
Dr. Hui Liu
Dr. Jun Xie
Dr. Lili Li
Dr. Min Li
Dr. João Rodrigues
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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

  • electroencephalogram
  • electromyogram
  • biomedical engineering
  • sensors
  • signal processing

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Published Papers (3 papers)

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Research

25 pages, 3241 KiB  
Article
Can EMG-Derived Upper Limb Muscle Synergies Serve as Markers for Post-Stroke Motor Assessment and Prediction of Rehabilitation Outcome?
by Fung Ting Kwok, Ruihuan Pan, Shanshan Ling, Cong Dong, Jodie J. Xie, Hongxia Chen and Vincent C. K. Cheung
Sensors 2025, 25(10), 3170; https://doi.org/10.3390/s25103170 - 17 May 2025
Viewed by 108
Abstract
EMG-derived muscle synergy, as a representation of neuromotor modules utilized for motor control, has been proposed as a biomarker for stroke rehabilitation. Here, we evaluate the utility of muscle synergies for assessing motor function and predicting post-intervention motor outcome in a stroke rehabilitation [...] Read more.
EMG-derived muscle synergy, as a representation of neuromotor modules utilized for motor control, has been proposed as a biomarker for stroke rehabilitation. Here, we evaluate the utility of muscle synergies for assessing motor function and predicting post-intervention motor outcome in a stroke rehabilitation clinical trial. Subacute stroke survivors (n = 59) received monthlong acupuncture (Acu), sham acupuncture (ShamAcu) or no acupuncture (NoAcu) as adjunctive rehabilitative intervention alongside standard physiotherapy. Clinical scores and EMGs (14 muscles, eight motor tasks) were collected from the stroke-affected upper limb before and after intervention. We then extracted muscle synergies from EMGs using non-negative matrix factorization and designed 12 muscle synergy indices (MSIs) to summarize different aspects of post-stroke synergy features. All MSIs correlated with multiple clinical scores, suggesting that our indices could potentially serve as biomarkers for post-stroke motor functional assessments. While the intervention groups did not differ in their pre-to-post differences in the clinical scores, the inclusion of MSIs into analysis revealed that on average Acu promoted more recovery of synergy features than ShamAcu and NoAcu, though not all subjects in the group were Acu responders. We then built regression models using pre-intervention MSIs and clinical variables to predict the outcomes of Acu and NoAcu and showed by a preliminary retrospective simulation of patient stratification that MSI-based predictions could have led to better post-intervention motor improvement. Overall, we demonstrate that muscle synergies can potentially clarify the effects of interventions and assist in motor assessment, outcome prediction, and treatment selection. Full article
(This article belongs to the Special Issue Electroencephalogram/Electromyogram-Based Sensing Technologies for Biomedical Applications: Challenges and Possible Applications)
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22 pages, 7546 KiB  
Article
Task-Independent Cognitive Workload Discrimination Based on EEG with Stacked Graph Attention Convolutional Networks
by Chenyu Wei, Xuewen Zhao, Yu Song and Yi Liu
Sensors 2025, 25(8), 2390; https://doi.org/10.3390/s25082390 - 9 Apr 2025
Viewed by 350
Abstract
In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than [...] Read more.
In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than optimal results. In this study, we present a stacked graph attention convolutional networks (SGATCNs) model to tackle the challenges related to task-independent cognitive workload assessment using EEG spatial information. The model employs the differential entropy (DE) and power spectral density (PSD) features of each EEG channel across four frequency bands (delta, theta, alpha, and beta) as node information. For the construction of the network structure, phase-locked values (PLVs), phase-lag indices (PLIs), Pearson correlation coefficients (PCCs), and mutual information (MI) are utilized and evaluated to generate a functional brain network. Specifically, the model aggregates spatial information on the dynamic map by stacking the graph attention layers and utilizes the convolution module to extract the frequency domain information from between the networks under each frequency band. We conducted a cognitive workload experiment with 15 subjects and selected three representative psychological experimental task paradigms (N-back, mental arithmetic, and Sternberg) to induce different levels of cognitive workload (low, medium, and high). Our framework achieved an average accuracy of 65.11% in recognizing the task-independent cognitive workload across the three scenarios. Full article
(This article belongs to the Special Issue Electroencephalogram/Electromyogram-Based Sensing Technologies for Biomedical Applications: Challenges and Possible Applications)
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26 pages, 7119 KiB  
Article
MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification
by Ling-Long Li, Guang-Zhong Cao, Yue-Peng Zhang, Wan-Chen Li and Fang Cui
Sensors 2024, 24(23), 7611; https://doi.org/10.3390/s24237611 - 28 Nov 2024
Viewed by 966
Abstract
Decoding lower-limb motor imagery (MI) is highly important in brain–computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain [...] Read more.
Decoding lower-limb motor imagery (MI) is highly important in brain–computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (LLMs) including MI are excessively close to physiological representations in the human brain and generate low-quality EEG signals. To address this challenge, this paper proposes a multidimensional attention-based convolutional neural network (CNN), termed MACNet, which is specifically designed for lower-limb MI classification. MACNet integrates a temporal refining module and an attention-enhanced convolutional module by leveraging the local and global feature representation abilities of CNNs and attention mechanisms. The temporal refining module adaptively investigates critical information from each electrode channel to refine EEG signals along the temporal dimension. The attention-enhanced convolutional module extracts temporal and spatial features while refining the feature maps across the channel and spatial dimensions. Owing to the scarcity of public datasets available for lower-limb MI, a specified lower-limb MI dataset involving four routine LLMs is built, consisting of 10 subjects over 20 sessions. Comparison experiments and ablation studies are conducted on this dataset and a public BCI Competition IV 2a EEG dataset. The experimental results show that MACNet achieves state-of-the-art performance and outperforms alternative models for the subject-specific mode. Visualization analysis reveals the excellent feature learning capabilities of MACNet and the potential relationship between lower-limb MI and brain activity. The effectiveness and generalizability of MACNet are verified. Full article
(This article belongs to the Special Issue Electroencephalogram/Electromyogram-Based Sensing Technologies for Biomedical Applications: Challenges and Possible Applications)
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