A wavelet based algorithm for the identification of oscillatory event-related potential components.

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.

journal_name

J Neurosci Methods

authors

Aniyan AK,Philip NS,Samar VJ,Desjardins JA,Segalowitz SJ

doi

10.1016/j.jneumeth.2014.06.004

subject

Has Abstract

pub_date

2014-08-15 00:00:00

pages

63-72

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(14)00210-6

journal_volume

233

pub_type

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