Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review-Reference-Cited by-同舟云学术

Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

Published:2018-04 Issue:02 Volume:10 Page:1840001
ISSN:2424-922X
Container-title:Advances in Data Science and Adaptive Analysis
language:en
Short-container-title:Adv. Data Sci. Adapt. Data Anal.

Author:

Sweeney-Reed Catherine M.¹^ORCID,Nasuto Slawomir J.²,Vieira Marcus F.³^ORCID,Andrade Adriano O.⁴^ORCID

Affiliation:

1. Neurocybernetics and Rehabilitation, Clinic for Neurology and Stereotactic Neurosurgery, Otto-von-Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany

2. Biomedical Engineering Section, School of Biological Sciences, University of Reading, Reading, RG6 6AY, UK

3. Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Brazil

4. Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health, Postgraduate Program in Electrical and Biomedical Engineering, Federal University of Uberlândia, Uberlândia, Brazil

Abstract

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.

Publisher

World Scientific Pub Co Pte Lt

Link

https://www.worldscientific.com/doi/pdf/10.1142/S2424922X18400016

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