Enhanced performance of EEG-based brain-computer interfaces by joint sample and feature importance assessment

Electroencephalograph (EEG) has been a reliable data source for building brain-computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG samples to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.