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EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition

  • Background: Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field. New method: EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis. Results: EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox. Comparison with existing methods: EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal. Conclusions: EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.

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Metadaten
Author:Markus GoldhackerORCiD, Karema Al-Subari, Saad Al-Baddai, Ana Maria ToméORCiD, Rupert Faltermeier, Elmar Wolfgang LangORCiD
DOI:https://doi.org/10.1016/j.jneumeth.2015.06.020
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/26162614
Parent Title (English):Journal of Neuroscience Methods
Publisher:Elsevier
Place of publication:AMsterdam
Document Type:Article
Language:English
Year of first Publication:2015
Release Date:2022/12/12
Volume:253
Issue:September
First Page:193
Last Page:205
Institutes:Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG