Cross-subject G-softmax deep domain generalization motor imagery classification in brain–computer interfaces

Background In cross-subject electroencephalography (EEG) motor imagery decoding tasks, significant physiological differences among individuals pose substantial challenges. Although Gaussian-based softmax Deep Domain Adaptation (DDA) methods have achieved considerable progress, they remain highly dependent on target domain data, which is inconsistent with real-world scenarios where target domain data may be inaccessible or extremely limited. Moreover, existing DDA methods primarily achieve feature alignment by minimizing distribution discrepancies between the source and target domains. However, given the pronounced physiological variability across individuals, simple distribution matching strategies often fail to effectively mitigate domain shift, thereby limiting generalization performance. Methods To address these challenges, this study proposes an improved Gaussian-based softmax Deep Domain Generalization (Exp-G-softmax DDG) framework, which aims to overcome the limitations of traditional DDG methods in handling inter-class differences and cross-domain distribution shifts. By introducing multi-source domain joint training and an enhanced G-softmax function, the proposed method effectively resolves the dynamic balance between intra-class distance and inter-class distance. The Exp-G-softmax DDG mechanism integrates class center information, thereby enhancing model robustness and improving its ability to learn discriminative feature representations, ultimately leading to superior classification performance. Results Experimental results demonstrate that the proposed method achieves classification performance comparable to that of DDA on three publicly available real-world EEG datasets, providing a novel solution for cross-subject motor imagery decoding. The source code is available at: https://github.com/dawin2015/G-softmax-DDG .