Cross-subject Trials Reweighting for Enhancing Motor Imagery-based Brain-Computer Interface

Because of the non-steady state of EEG signals, there are differences in the distribution of electroencephalogram (EEG) data among different subjects. This distribution difference leads to a large error indirectly using the data of different subjects for training. This paper proposes a cross-subject trial reweighting (CSTR) method to reduce the distribution difference. CSTR assigns weights to each sample to narrow the maximum mean discrepancy between the source and target domains. CSTR is applied to the original EEG data samples, and can also be applied to samples after various feature processing. We use the motor imagery dataset to verify the effectiveness of the CSTR algorithm. The experimental results show that the source domain and the target domain become more similar after the trials are reweighted. The classification performance is improved using the reweighted data. The method proposed in this paper can improve the performance of the transfer learning brain-computer interface, reduce the calibration time of BCI, and promote the practical application of BCI.