Bi-Stream Adaptation Network for Motor Imagery Decoding.

Neural activities in distinct brain regions variably contribute to the formation of motor imagery (MI). Utilizing the hidden contextual information can thereby enhance network performance by having a comprehensive understanding of MI. Besides, due to the non-stationarity of EEG, the global and local distributions of cross-session EEG from an individual vary in applications. Based on these ideas, a novel Bi-Stream Adaptation Network (BSAN) is proposed to generate multi-scale context dependencies and to bridge the cross-session discrepancies in MI classification. Specifically, a Bi-attention module is proposed to cultivate multi-scale temporal dependencies and figure out the predominant brain regions. After features extraction, a Bi-discriminator is trained to implement the task of domain adaptation both globally and locally. To validate the proposed BSAN, extensive experiments were conducted based on two public MI datasets. The results revealed that the proposed BSAN improved the performance and robustness of MI classification and outperformed several state-of-the-art methods.