Automatic identification and removal of artifacts in EEG using a probabilistic multi-class SVM approach with error correction
Shao, S.-Y. Shen, K.-Q. Ong, C.-J. Li, X.-P. Wilder-Smith, E.P.V.
Wilder-Smith, E.P.V.
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Abstract
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. © 2008 IEEE.
Keywords
Artifact removal, Electroencephalogram, Error correction, Support vector machine
Source Title
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
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Date
2008
DOI
10.1109/ICSMC.2008.4811434
Type
Conference Paper