A statistical comparison of EEG time- and time-frequency domain representations of error processing

Abstract

Item does not contain fulltextSuccessful behavior relies on error detection and subsequent remedial adjustment of behavior. Researchers have identified two electrophysiological signatures of error processing: the time-domain error-related negativity (ERN), and the time-frequency domain increased power in the delta/theta frequency bands (~2-8 Hz). The relationship between these two signatures is not entirely clear: on the one hand they occur after the same type of event and with similar latency, but on the other hand, the time-domain ERP component contains only phase-locked activity whereas the time-frequency response additionally contains non-phase-locked dynamics. Here we examined the ERN and error-related delta/theta activity in relation to each other, focusing on within-subject analyses that utilize single-trial data. Using logistic regression, we constructed three statistical models in which the accuracy of each trial was predicted from the ERN, delta/theta power, or both. We found that both the ERN and delta/theta power worked roughly equally well as predictors of single-trial accuracy (~70% accurate prediction). Furthermore, a model including both measures provided a stronger overall prediction compared to either model alone. Based on these findings two conclusions are drawn: first, the phase-locked part of the EEG signal appears to be roughly as predictive of single-trial response accuracy as the non-phase-locked part; second, the single-trial ERP and delta/theta power contain both overlapping and independent information