Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Abstract

Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. The selection of regions of evoked EROs tends to be subjectively based on the previous studies and the visual inspection of grand averaged time-frequency representations (TFRs) which causes some missing or redundant information. Meanwhile, the evoked EROs cannot be fully extracted via the conventional time-frequency analysis (TFA) method because they are sometimes overlapped with each other or with artifacts in time, frequency, and space domains to some extent. Hence, these shortcomings may pose some challenges to investigate the related neuronal processes. A data-driven approach was introduced to fill the gaps as below: extracting the temporal and spatial components of interest simultaneously by principal component analysis and Promax rotation and projecting them to the electrode field to correct their variance and polarity indeterminacy, calculating the TFRs of the back-projected components, and determining the regions of interest objectively using the edge detection algorithm. We performed this novel approach and the conventional TFA method in analyzing both a synthetic dataset and an actual ERP dataset in a two-factor simple gambling paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic dataset results indicated that N2-theta and P3-delta oscillations were detected using the proposed approach, but, by comparison, only one oscillation was obtained via the conventional TFA method. Furthermore, the actual ERP dataset results of P3-delta for our approach revealed that it was sensitive to the waiting time (which also was found in the previous reports) but not for that of the conventional TFA method. This study manifested that the proposed approach can objectively extract evoked EROs, which allows a better understanding of the modulations of the oscillatory responses.