Evaluation of Empirical Mode Decomposition for Event-Related Potential Analysis. (10th March 2011)

• Record Type: Journal Article
• Authors: Williams Williams, N. N.; Nasuto Nasuto, S. J. S. J.; Saddy Saddy, J. D. J. D.
• Other Names: Flandrin Flandrin Patrick Patrick Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2011(2011)
• Publication Date: 2011-03-10
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2011/965237
• Languages: English
• ISSNs: 1687-6172

Abstract

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.