Automatic identification and removal of artifacts in EEG using a probabilistic multi-class SVM approach with error correction

A novel electroencephalogram (EEG) artifact removal method is presented in this paper. The proposed method combines a probabilistic multi-class Support Vector Machine (SVM) and an error correction algorithm for component classification, where i) the probabilistic multi-class SVM is modified to prope...

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Bibliographic Details
Main Authors: Shi-Yun Shao, Kai-Quan Shen, Chong-Jin Ong, Xiao-Ping Li, Wilder-Smith, E.P.V.
Format: Conference Proceeding
Language:English
Subjects:
Artifact removal
Classification algorithms
electroencephalogram
Electroencephalography
Error correction
Hospitals
Independent component analysis
Machine learning
Mechanical engineering
Nervous system
support vector machine
Support vector machine classification
Support vector machines
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Description
Summary:A novel electroencephalogram (EEG) artifact removal method is presented in this paper. The proposed method combines a probabilistic multi-class Support Vector Machine (SVM) and an error correction algorithm for component classification, where i) the probabilistic multi-class SVM is modified to properly handle the unbalanced nature of component classification and ii) the error correction algorithm is used to accommodate the structural information of the learning problem. The proposed component classifier was tested on real-life EEG data and it significantly outperformed the standard SVM used in the literature. A qualitative evaluation on the reconstructed EEG shows that the proposed artifact removal method greatly reduced the amount of artifacts while well preserving brain activities in almost all EEG epochs.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2008.4811434