Spatio-Temporal Deep Learning with Adaptive Attention for EEG and sEMG Decoding in Human–Machine Interaction

• Published in: Electronics (Basel) Vol. 14; no. 13; p. 2670
• Main Authors: Fu, Tianhao, Zhou, Zhiyong, Yuan, Wenyu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2025
• Subjects: Ablation; Accuracy; Analysis; Brain research; Channels; Classification; Computational linguistics; Datasets; Deep learning; Design; Electroencephalography; Electromyography; Fourier transforms; Gesture recognition; Interfaces; Language processing; Machine learning; Modules; Natural language interfaces; Neural networks; Prostheses; Real time; New Jersey
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics14132670

Electroencephalography (EEG) and surface electromyography (sEMG) signals are widely used in human–machine interaction (HMI) systems due to their non-invasive acquisition and real-time responsiveness, particularly in neurorehabilitation and prosthetic control. However, existing deep learning approaches often struggle to capture both fine-grained local patterns and long-range spatio-temporal dependencies within these signals, which limits classification performance. To address these challenges, we propose a lightweight deep learning framework that integrates adaptive spatial attention with multi-scale temporal feature extraction for end-to-end EEG and sEMG signal decoding. The architecture includes two core components: (1) an adaptive attention mechanism that dynamically reweights multi-channel time-series features based on spatial relevance, and (2) a multi-scale convolutional module that captures diverse temporal patterns through parallel convolutional filters. The proposed method achieves classification accuracies of 79.47% on the BCI-IV 2a EEG dataset (9 subjects, 22 channels) for motor intent decoding and 85.87% on the NinaPro DB2 sEMG dataset (40 subjects, 12 channels) for gesture recognition. Ablation studies confirm the effectiveness of each module, while comparative evaluations demonstrate that the proposed framework outperforms existing state-of-the-art methods across all tested scenarios. Together, these results demonstrate that our model not only achieves strong performance but also maintains a lightweight and resource-efficient design for EEG and sEMG decoding.