A Wavelet Based Algorithm for the Identification of Oscillatory Event-Related Potential Components

TL;DR

This paper introduces a wavelet-based algorithm that accurately identifies ERP components in single-trial EEG data, outperforming existing methods and adaptable to various ERP types.

Contribution

A novel wavelet-based algorithm utilizing time-based parameters and asymmetry properties for improved ERP component detection in EEG signals.

Findings

High accuracy in detecting N170 ERP component

Asymmetry method outperforms matching wavelet and t-CWT methods

Potential for real-time application extension

Abstract

Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.