Multi-Source Discriminant Dynamic Domain Adaptation for Cross-Subject Motor Imagery EEG Recognition

Electroencephalography (EEG) has emerged as a widely utilized signal in motor imagery (MI) brain-computer interfaces(BCI) due to its convenience and safety. Recently, deep learning methods have rapidly developed in the field of brain computer interfaces. However, traditional EEG classification methods often face challenges related to limited generalization capability across subjects. To address this issue, this paper proposes a multi-source discriminant dynamic domain adaptation model(MSD-DDA) aimed at fully leveraging domain adaptation to enhance the accuracy of motor imagery classification. The model adeptly handles global and local disparities in motor imagery classification by dynamically minimizing differences between global domain and local subdomain. Furthermore, to ensure discriminability and diversity in the target domain, we introduce batch kernel norm maximization of the difference, thereby enhancing the model's discriminability in the target domain while preserving prediction diversity. To tackle variations in similarity between different source domains and the target domain, we devise a weighted joint prediction mechanism. This mechanism automatically adjusts the contribution weight of each source domain based on its similarity to the target domain, facilitating more precise discriminant prediction and improved adaptability to scenarios with multiple source domains. To evaluate our approach, we conducted a large number of experiments on datasets 1 and 2a of the Fourth BCI Competition and on the openBMI dataset, with average classification accuracy of 92.43%, 79.24% and 71.96%, respectively.Finally, we compare the proposed method with several classical and recent algorithms, and prove that its performance is better than the existing methods.