Review Article
Open Access
Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis
1
Laboratoire de Biomécanique et de Bioingénierie UMR CNRS, Université de technologie de Compiégne, Compiégne, France
* Corresponding Author:
Sidi Mohamed Sid'El Moctar,
[email protected]
Volume 1, Issue 2
2025
Received: 13 November 2025
Accepted: 21 December 2025
Published: 24 December 2025
Article Information
Published in
Journal of Artificial Intelligence in Bioinformatics
Volume/Issue
Volume 1, Issue 2, 2025
Pages
79-84
Abstract
Electromyography (EMG) signals provide critical insights into neuromuscular function, yet their analysis remains challenging due to inherent noise, inter-subject variability, and non-stationary characteristics. Bio-inspired artificial intelligence (AI) models, drawing computational principles from biological neural systems, offer promising solutions to these challenges. This mini-review synthesizes recent advances in bio-inspired AI approaches for EMG signal processing, including spiking neural networks, hierarchical deep learning, attention mechanisms, and neuromorphic computing. We evaluate state-of-the-art methods, comparing their performance across key metrics including classification accuracy, computational efficiency, and real-world applicability. Our analysis reveals that hybrid architectures combining convolutional neural networks with transformer-based attention mechanisms achieve superior performance while maintaining computational efficiency. We identify emerging trends in multimodal integration, self-supervised learning, and edge computing implementations. This paper provides researchers and practitioners with a comprehensive framework for selecting appropriate bio-inspired AI methods for specific EMG applications in prosthetics, clinical diagnosis, rehabilitation, and human-computer interaction.
Graphical Abstract
Keywords
electromyography
bio-inspired AI
deep learning
signal processing
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declares no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
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APA Style
Sid’El Moctar, S. M. (2025). Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis. Journal of Artificial Intelligence in Bioinformatics, 1(2), 79–84. https://doi.org/10.62762/JAIB.2025.677230
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TY - JOUR AU - Moctar, Sidi Mohamed Sid'El PY - 2025 DA - 2025/12/24 TI - Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis JO - Journal of Artificial Intelligence in Bioinformatics T2 - Journal of Artificial Intelligence in Bioinformatics JF - Journal of Artificial Intelligence in Bioinformatics VL - 1 IS - 2 SP - 79 EP - 84 DO - 10.62762/JAIB.2025.677230 UR - https://www.icck.org/article/abs/JAIB.2025.677230 KW - electromyography KW - bio-inspired AI KW - deep learning KW - signal processing AB - Electromyography (EMG) signals provide critical insights into neuromuscular function, yet their analysis remains challenging due to inherent noise, inter-subject variability, and non-stationary characteristics. Bio-inspired artificial intelligence (AI) models, drawing computational principles from biological neural systems, offer promising solutions to these challenges. This mini-review synthesizes recent advances in bio-inspired AI approaches for EMG signal processing, including spiking neural networks, hierarchical deep learning, attention mechanisms, and neuromorphic computing. We evaluate state-of-the-art methods, comparing their performance across key metrics including classification accuracy, computational efficiency, and real-world applicability. Our analysis reveals that hybrid architectures combining convolutional neural networks with transformer-based attention mechanisms achieve superior performance while maintaining computational efficiency. We identify emerging trends in multimodal integration, self-supervised learning, and edge computing implementations. This paper provides researchers and practitioners with a comprehensive framework for selecting appropriate bio-inspired AI methods for specific EMG applications in prosthetics, clinical diagnosis, rehabilitation, and human-computer interaction. SN - 3068-7535 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Moctar2025BioInspire,
author = {Sidi Mohamed Sid'El Moctar},
title = {Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis},
journal = {Journal of Artificial Intelligence in Bioinformatics},
year = {2025},
volume = {1},
number = {2},
pages = {79-84},
doi = {10.62762/JAIB.2025.677230},
url = {https://www.icck.org/article/abs/JAIB.2025.677230},
abstract = {Electromyography (EMG) signals provide critical insights into neuromuscular function, yet their analysis remains challenging due to inherent noise, inter-subject variability, and non-stationary characteristics. Bio-inspired artificial intelligence (AI) models, drawing computational principles from biological neural systems, offer promising solutions to these challenges. This mini-review synthesizes recent advances in bio-inspired AI approaches for EMG signal processing, including spiking neural networks, hierarchical deep learning, attention mechanisms, and neuromorphic computing. We evaluate state-of-the-art methods, comparing their performance across key metrics including classification accuracy, computational efficiency, and real-world applicability. Our analysis reveals that hybrid architectures combining convolutional neural networks with transformer-based attention mechanisms achieve superior performance while maintaining computational efficiency. We identify emerging trends in multimodal integration, self-supervised learning, and edge computing implementations. This paper provides researchers and practitioners with a comprehensive framework for selecting appropriate bio-inspired AI methods for specific EMG applications in prosthetics, clinical diagnosis, rehabilitation, and human-computer interaction.},
keywords = {electromyography, bio-inspired AI, deep learning, signal processing},
issn = {3068-7535},
publisher = {Institute of Central Computation and Knowledge}
}
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Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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