Objective Extraction of Evoked Event-Related Oscillation 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. In most previous studies, the calculation of the regions of evoked EROs of interest is commonly based on a predefined time window and a frequency range given by the experimenter, which tends to be subjective. Additionally, evoked EROs sometimes cannot be fully extracted using the conventional time-frequency analysis (TFA) because they may be overlapped with each other or with artifacts in time, frequency, and space domains. To further investigate the related neuronal processes, a novel approach was proposed including three steps: (1) extract the temporal and spatial components of interest simultaneously by temporal principal component analysis (PCA) and Promax rotation and project them to the electrode fields for correcting their variance and polarity indeterminacies, (2) calculate the time-frequency representations (TFRs) of the back-projected components, and (3) compute the regions of evoked EROs of interest on TFRs objectively using the edge detection algorithm. We performed this novel approach, conventional TFA, and TFA-PCA to analyse both the synthetic datasets with different levels of SNR and an actual ERP dataset in a two-factor paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic datasets results indicated that N2-theta and P3-delta oscillations can be stably detected from different SNR-simulated datasets using the proposed approach, but, by comparison, only one oscillation was obtained via the last two approaches. Furthermore, regarding the actual dataset, the statistical results for the proposed approach revealed that P3-delta was sensitive to the waiting time but not for that of the other approaches. This study manifested that the proposed approach could objectively extract evoked EROs of interest, which allows a better understanding of the modulations of the oscillatory responses.

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

“…In practice, the back-projection is to calculate the sums of the outer-product between temporal and spatial PCA-components for reconstructing the waveforms of desired ERPs. As shown in our recent study (Zhang et al, 2020), PCA is indeed a BSS approach when the sources are assumed to be correlated with each other and the back projection theory also works for PCA like that for ICA.…”

“…Those epochs' datasets whose magnitudes exceeded ±80μV were discarded and the remaining epochs were baseline corrected. Finally, EEG data of all epochs were filtered by wavelet filter (Cong, Ristaniemi, & Lyytinen, 2015;Zhang et al, 2020) to improve signal-to-noise ratio (SNR). The parameters were set for wavelet filter as below: the number of decomposition level was 10; the select mother wavelet was 'rbio6.8'; the detail coefficients at levels 5, 6, 7, 8, and 9 were used for signal reconstruction.…”

“…The application of PCA to a spatial-stacked matrix (the rows are time points and the channels from each condition/subject/trial (optional) are in columns) obtained from EEG dataset of either single subject or multiple subjects can be illustrated by Eq. (1) (Zhang et al, 2020). In Eq.…”

“…In Eq. (1), the number of observed signals is larger than that of sources, i.e., it is an over-determined model, and we usually change this over-determined model to determined model by some methods, for example, the accumulative explained variance (Zhang et al, 2020).…”

“…In this study, we take the decomposition of spatialconcatenation matrix as an example. The matrix can be mathematically expressed as below (Zhang, Li, & Cong, 2020):…”