Evaluation of Empirical Mode Decomposition for Event-Related Potential Analysis

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 substantial...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2011-03, Vol.2011 (1), p.965237-965237
Main Authors: Williams, N, Nasuto, S J, Saddy, J D
Format: Article
Language:English
Subjects:
Decomposition
Fourier analysis
Mean square errors
Methods
Neurosciences
Principal components analysis
Studies
Wavelet transforms
Online Access:Get full text
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Summary: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.
ISSN:1687-6172
1687-6180
1687-6180
DOI:10.1186/1687-6180-2011-965237