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Empirical mode decomposition and its extensions applied to EEG analysis: a review

Sweeney-Reed, C. M., Nasuto, S. J., Vieira, M. F. and Andrade, A. O. (2018) Empirical mode decomposition and its extensions applied to EEG analysis: a review. Advances in Data Science and Adaptive Analysis, 10 (02). 1840001. ISSN 2424-922X

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To link to this item DOI: 10.1142/s2424922x18400016

Abstract/Summary

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:78831
Publisher:World Scientific Publishing

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