Detection of EEG spatial-spectral-temporal signatures of errors: a comparative study of ICA-based and channel-based methods.

Abstract

The present study aimed to investigate the sensitivity of independent component analysis (ICA)- and channel-based methods in detecting electroencephalography (EEG) spatial-spectral-temporal signatures of performance errors. 128-channel EEG signals recorded from 18 subjects, who performed a color-word matching Stroop task, were analyzed. The spatial-spectral-temporal patterns in event-related potentials (ERPs) and oscillatory activities (i.e., power and phase) were measured at four selected channels, i.e., FCz, Pz, O1 and O2, from original EEG data after preprocessing, EEG data after additional current source density (CSD) transform, and back-projected EEG data from individual ICs after additional ICA analysis. Pair-wise correlation coefficient (CC) and mutual information (MI), calculated from three EEG data at four selected channels, were compared to examine mutual correlations in EEG signals obtained through three different means. Thereafter, EEG signatures of errors from these three means were statistically compared at multiple time windows in the contrast of error and correct responses. Significantly decreased CC and MI values were observed in CSD- and ICA-processed EEGs as compared with original EEG, with the smallest CC and MI in ICA EEG. Similar error patterns in ERPs and peri-response oscillatory activities were detected in all three EEGs, whereas the pre-stimulus and post-stimulus error-related oscillatory patterns identified in ICA EEG were either not or only partially detected in both original EEG and CSD EEGs in general. Both CSD and ICA processes can largely reduce signal correlations due to the volume conduction effect in original EEG, and EEG signatures of errors are better detected by ICA-based method than channel-based method (i.e., original and CSD EEGs). ICA provides the best sensitivity to detect EEG signatures linked to specific neural processes via disentangling superimposed channel-level EEG signals into distinct neurocognitive process-related component signals.