Motor Imagery Decoding from EEG Under Visual Distraction Via Feature Map Attention EEGNet

The investigation of motor imagery (MI)-based brain-computer interface (BCI) is vital to the domains of human-computer interaction and rehabilitation. Few existing studies on electroencephalogram(EEG) signals decoding based on MI consider any distractions. However, it is difficult for users to do a single MI task in real life, which is especially affected by visual distraction. In this paper, we aim to investigate the effects of visual distraction on MI decoding performance. We first design a noval MI paradigm under visual distraction and observe distinct patterns of event-related desynchronization (ERD) and event-related synchronization (ERS) in MI under visual distraction. Then, we propose a robust decoding method of MI under visual distraction from EEG signals by using the feature map attention EEGNet (named FMA-EEGNet) and use EEG data under conditions without and with distraction to compare the decoding performance of five methods (including the proposed method and other methods). The results demonstrate that FMA-EEGNet achieved mean accuracy of 89.1% and 82.2% without and with visual distraction, respectively, indicating superior performance compared to other methods while exhibiting minimal degradation in performance. This work contributes significantly to the advancement of practical applications in MI-BCI technology.