Abstract:
This paper presents a combination of Independent Component Analysis (ICA) with Empirical Mode Decomposition (EMD) to suppress muscle and ocular artifacts in electroenceph...Show MoreMetadata
Abstract:
This paper presents a combination of Independent Component Analysis (ICA) with Empirical Mode Decomposition (EMD) to suppress muscle and ocular artifacts in electroencephalographic (EEG) signals: By means of ICA, the EEG signals are decomposed into independent components. To avoid the suppression of artifactual components still containing physiological information, EMD is applied to decompose the components in Intrinsic Mode Functions (IMFs). The IMFs with mainly muscle artifacts are removed, and a new data set of independent components without muscle artifacts is generated. From this set, the components containing ocular artifacts are suppressed and clean data are reconstructed. In this way, the muscle and ocular artifacts are better suppressed than using pure ICA, or pure EMD. The performance of the proposed combination is applied to a semi-simulated data set, and three real EEG data sets from healthy subjects contaminated with both artifacts.
Published in: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 18-21 July 2018
Date Added to IEEE Xplore: 28 October 2018
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PubMed ID: 30440617