Multi-Domain Feature Fusion and Channel Attention in an Inception-Based Architecture for Motor Imagery EEG Decoding

Abstract

Background/Objectives: Existing motor imagery (MI) EEG decoding is often limited by small datasets, affecting generalization reliability. This study aims to robustly decode multi-limb MI intentions. Methods: We collected an MI-EEG dataset from 292 participants (242 young adults, 50 older adults) performing left/right-arm and left/right-leg imagery. After extracting time-, frequency-, and channel correlation features, we proposed an SE-EEG-Inception model for classification. Results: Evaluated under a strict intra-subject cross-validation protocol, the model achieved a mean 4-class accuracy of 89.4%. For binary tasks, accuracies reached 88.3% (left vs. right arm) and 90.0% (left vs. right leg). Conclusions: The model successfully distinguishes predictive EEG features across different and symmetric limbs. Crucially, this high classification performance demonstrates data-driven predictive utility rather than mechanistic proof of neural differences, providing an offline proof-of-concept for multi-limb BCI control.