Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis

Sidi Mohamed Sid'El Moctar

doi:10.62762/JAIB.2025.677230

Volume 1, Issue 2, Journal of Artificial Intelligence in Bioinformatics

Volume 1, Issue 2, 2025

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Journal of Artificial Intelligence in Bioinformatics, Volume 1, Issue 2, 2025: 79-84

Open Access | Review Article | 24 December 2025

Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis

Sidi Mohamed Sid'El Moctar 1 *

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]

DOI: 10.62762/JAIB.2025.677230

ARK: ark:/57805/jaib.2025.677230

Received: 13 November 2025, Accepted: 21 December 2025, Published: 24 December 2025

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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

Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis

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.

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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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