Manifold Trial Selection to Reduce Negative Transfer in Motor Imagery-based Brain-Computer Interface

A major challenge in electroencephalogram (EEG) signal classification is that the EEG signals recorded from different subjects are drawn from different distributions. When the unlabeled EEG data of the new subject arrive, called target domain, classifying them with a classifier trained on prerecorded EEG data of other subjects, called source domain, will greatly decrease the classification accuracy. Being able to use the classifiers trained on data of source domain to accurately classify the data of target domain could reduce the time of the calibration phase in the actual application of the brain-computer interface. This study considers an offline cross-subject classification scenario. We propose a novel manifold trial selection method, which reduces the distribution distance between the source and target domains by manifold transformation and domain adaptation. The proposed method provides a trial selection strategy to suppress negative transfer by removing some abnormal samples. The proposed method is applied to the motor imagery-based brain-computer interface and compared with several existing algorithms. Experimental results show that the proposed method outperforms the state-of-the-art methods.