A Novel Domain Adversarial Framework for Improving Cross-Subject Motor Imagery Classification

Motor imagery classification plays a crucial role in brain-computer interfaces by decoding electroencephalogram (EEG) signals associated with motor imagery and enabling control of external devices. Existing methods often face challenges in generalizing to new subjects due to variations in brain activity patterns. To address this issue, we propose a novel multi-domain adversarial framework that learns task-related representations while being unrelated of subject differences. Our framework incorporates multiple domain adversarial discriminators and introduces a unique adversarial training strategy to align feature distributions across subjects, thereby optimizing classification objectives. Through extensive cross-subject experiments on the widely used BCI Competition IV-2a dataset, we demonstrate the effectiveness of our approach, achieving an average improvement in classification accuracy. These findings indicate the potential of our framework to advance motor imagery classification, benefiting areas such as human-computer interaction, automatic control, and medical sports rehabilitation.