A Novel EEG Correlation Coefficient Feature Extraction Approach Based on Demixing EEG Channel Pairs for Cognitive Task Classification

This paper presents a novel feature extraction method for electroencephalogram (EEG)-based cognitive task classification based on the correlation coefficients of EEG channel pairs by introducing preprocessing of the EEG signals. The preprocessing attempts to optimally demix each pair of EEG channels using a two-dimensional rotation matrix in order to mitigate the interference between channel pairs and, consequently, to enhance the resulting correlation coefficient features for cognitive task classification. For the optimization, the following criteria are proposed with an optimal rotation angle approximated for each criterion: i ) maximum inter-class correlation coefficient distance (ICCD); ii ) minimum within-class correlation coefficient distance (WCCD); and iii ) maximum Fisher ratio (FR), which is the ratio of ICCD to WCCD. Performance evaluation based on the cognitive task dataset, dataset IV and Ib in BCI competition II, and Keirn and Aunon's dataset, shows that ICCD optimization with the 'above the mean' and 1.5 interquartile range (IQR) feature selection method yields the best classification performance in comparison with other existing cognitive task classification methods.