Single-trial-based Temporal Principal Component Analysis on Extracting Event-related Potentials of Interest for an Individual Subject

Abstract

Temporal principal component analysis (t-PCA) has been widely used to extract event-related potentials (ERPs) at the group level of multiple subjects’ ERP data. The t-PCA is, however, poorly employed to isolate ERPs from single-trial data of an individual subject. Additionally, the effects of varied trial numbers on the yields of t-PCA have not been systematically examined. To fill both gaps, in an emotional experiment (22 subjects), we use t-PCA and Promax rotation to extract interesting P2/N2 from single-trial data of each subject with consecutive increasingly trial numbers (from 10 to 42) and all trials, respectively. Besides, time-domain analysis and other two group t-PCA strategies (trial-averaged and single-trial) are also employed to isolate ERPs of interest from all subjects. The results indicate that the proposed technique produces the internal consistent measure of N2 from few trials (i.e., 19) as from all trials compared with the other three approaches (more than 30 trials). As for P2, all approaches yield internal-subject consistent effect after approximately 33 trials are included in the average, but Cronbach’s alpha values for the proposed technique are higher than the other two group PCA strategies over varied trials. Combined, the yields provide evidence that the proposed approach may efficiently temporally filter the data to extract more reliable and stable ERPs for an individual subject.