Open Access

Evaluation of Empirical Mode Decomposition for Event-Related Potential Analysis

EURASIP Journal on Advances in Signal Processing20112011:965237

DOI: 10.1155/2011/965237

Received: 2 July 2010

Accepted: 21 January 2011

Published: 10 March 2011

Abstract

Current methods for estimating event-related potentials (ERPs) assume stationarity of the signal. Empirical Mode Decomposition (EMD) is a data-driven decomposition technique that does not assume stationarity. We evaluated an EMD-based method for estimating the ERP. On simulated data, EMD substantially reduced background EEG while retaining the ERP. EMD-denoised single trials also estimated shape, amplitude, and latency of the ERP better than raw single trials. On experimental data, EMD-denoised trials revealed event-related differences between two conditions (condition A and B) more effectively than trials lowpass filtered at 40 Hz. EMD also revealed event-related differences on both condition A and condition B that were clearer and of longer duration than those revealed by low-pass filtering at 40 Hz. Thus, EMD-based denoising is a promising data-driven, nonstationary method for estimating ERPs and should be investigated further.

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Authors’ Affiliations

(1)
Centre for Integrative Neuroscience and Neurodynamics (CINN), University of Reading

Copyright

© N. Williams et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.