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.
@article{arxiv.1407.2227,
title = {A Wavelet Based Algorithm for the Identification of Oscillatory Event-Related Potential Components},
author = {Arun Kumar A and Ninan Sajeeth Philip and Vincent J Samar and James A Desjardins and Sidney J Segalowitz},
journal= {arXiv preprint arXiv:1407.2227},
year = {2014}
}