Evaluation of Empirical Mode Decomposition for event-related potential analysis

N. Williams*, S. J. Nasuto, J. D. Saddy

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)

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 40Hz. 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 40Hz. Thus, EMD-based denoising is a promising data-driven, nonstationary method for estimating ERPs and should be investigated further.

Original languageEnglish
Article number965237
JournalEurasip Journal on Advances in Signal Processing
Volume2011
DOIs
Publication statusPublished - 2011
MoE publication typeA1 Journal article-refereed

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