Bio-Inspired Machine Learning for Enhanced EMG Signal Analysis

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.