Abstract
Advances in artificial intelligence have significantly enhanced intelligent assistance and rehabilitation medicine by leveraging electroencephalogram (EEG) signal recognition. Nevertheless, eliminating cross-subject variability remains a significant challenge in expending the application of EEG signal recognition to the broader society. The transfer learning strategy has been utilized to address this issue; however, multi-source domains are often treated as a single entity in transfer learning, leading to underutilization of the information from multiple sources. Furthermore, many EEG signal transfer approaches overlook the low-dimensional structural information and multivariate statistical features inherent in EEG signals, leading to inadequate interpretability and suboptimal performance. Thus, in this study, a novel multi-morphological representation approach (MMRA) was proposed for multi-source EEG signal recognition to address these issues. MMRA utilized multi- manifold mapping to extract the common invariant representation shared between the multi-source domains and target domain. It took into account the low-dimensional structure and multivariate statistical features of EEG signals to enhance the acquisition of high-quality common invariant representations. Subsequently, the multi-source domains were decomposed to extract one-to-one features. The Maximum Mean Discrepancy (MMD) loss was further applied to guide the model in obtaining high-quality private invariant representations. The performance of the proposed MMRA method was evaluated using three publicly available motor imagery datasets and a driving fatigue dataset. Experimental results demonstrated that our proposed MMRA method outperformed other state-of-the-art methods in scenarios involving multiple subjects. In conclusion, the MMRA method developed in this study can serve as a novel tool offering enhanced performance to analyze EEG signals across various subjects.