Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques

In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and Independent Component Analysis (ICA). The signal...

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Bibliographic Details
Published in:arXiv.org 2014-10
Main Authors: Bono, Valentina, Wasifa Jamal, Das, Saptarshi, Maharatna, Koushik
Format: Article
Language:English
Subjects:
Algorithms
Blinking
Electroencephalography
Empirical analysis
Independent component analysis
Muscles
Shaking
Wavelet analysis
Wavelet transforms
Online Access:Get full text
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Summary:In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and Independent Component Analysis (ICA). The signal cleaning performances of WPT-EMD and WPT-ICA algorithms have been compared using a signal-to-noise ratio (SNR)-like criterion for artifacts. The algorithms have been tested on multiple trials of four different artifact cases viz. eye-blinking and muscle artifacts including left and right hand movement and head-shaking.
ISSN:2331-8422
DOI:10.48550/arxiv.1410.5801