SCDAN: Learning Common Feature Representation of Brain Activation for Intersubject Motor Imagery EEG Decoding

An electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface (BCI) builds a direct communication channel between humans and computers by decoding EEG signals. The intersubject decoding ability is crucial for the application of MI-BCI, which implies that the subject can use MI-BCI equipment without recording additional data for training. Physiologically, because of the distinction in the imagery method, brain structure, and brain state, the intersubject data distribution of MI EEG data is different. This often leads to a partial or even complete failure of the MI decoding algorithm between subjects. To solve these issues, we propose a novel deep learning method called the spatial and conditional domain adaption network (SCDAN), which aims to adapt the intersubject MI EEG data. In SCDAN, three innovative structures are employed: a parallel temporal-spatial convolution feature extractor, a spatial discriminator, and a conditional discriminator. The feature extractor adopts an improved temporal-spatial convolutional network that has a more reasonable structure and fewer parameters to reduce the risk of intersubject overfitting. The spatial discriminator and conditional discriminator calibrate the training processing to help the feature extractor learn the intersubject common feature representation. We evaluate the performance of SCDAN on the GigaScience dataset and the 2a BCI Competition IV dataset using both one-to-one and leave-one-out transfer protocols. For the one-to-one transfer protocol, the classification accuracies of SCDAN improve by 5.70% and 12.43% compared with the baseline method. And for the leave-one-out transfer protocol, the improvements are 8.60% and 15.84%, respectively. The results show a significant improvement compared with the baseline and comparison methods.