Accessibility navigation


Evaluation of empirical mode decomposition for event-related potential analysis

Williams, N., Nasuto, S. and Saddy, J. D. ORCID: https://orcid.org/0000-0001-8501-6076 (2011) Evaluation of empirical mode decomposition for event-related potential analysis. EURASIP Journal on Advances in Signal Processing, 2011. 965237. ISSN 1687-6180

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1155/2011/965237

Abstract/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.

Item Type:Article
Refereed:Yes
Divisions:Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Interdisciplinary Research Centres (IDRCs) > Centre for Integrative Neuroscience and Neurodynamics (CINN)
Life Sciences > School of Psychology and Clinical Language Sciences > Department of Clinical Language Sciences
ID Code:28671
Publisher:Springer

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation