Improved Motor Imagery EEG Interdevice Decoding by Reweighting Multisource Domain Samples

Electroencephalogram (EEG)-based motor imagery brain-computer interface (MI BCI) has exciting prospects in applications. Multisource domain problem of MI EEG decoding needs to be solved urgently. That is, how to use existing vast amounts of MI EEG data (multisource domain) to train interdevice algorithms for new equipment (target domain) decoding. In this work, we propose a compact sample reweighting EEG decoding network (SRENet) method and a sample reweighting training strategy to solve this issue. The target domain is expressed as a weighted combination of multisource domains to improve the decoding performance of interdevice MI. A novel sample reweighting classifier and a conditional reweighting discriminator are used for reweighting multisource domain samples in training process. We evaluated the performance of SRENet on three public datasets. The results outperformed baseline method by 6.88%, 5.90%, and 3.49% on the three tasks, respectively. Experimental results verified the effectiveness of the proposed method for multisource domain problems. The interdevice MI performance has been significantly improved. This study provides a new solution for multisource domain problem in MI EEG decoding, which will make better use of existing EEG datasets and help people use BCI more easily.