Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 10, Issue -, Pages 250-259Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2013.10.006
Keywords
Electroencephalogram; Ocular artifact; Independent Component Analysis; System identification; Auto-Regressive exogenous
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Funding
- National Nature Science Foundation of China [61175117, 31070881, 31100745]
- 863 project [2012AA011601]
- program for New Century Excellent Talents in University [NCET-12-0089]
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Eye activity is one of the main sources of artifacts in electroencephalogram (EEG) recordings, however, the ocular artifact can seriously distort the EEG recordings. It is an open issue to remove the ocular artifact as completely as possible without losing the useful EEG information. Independent Component Analysis (ICA) has been one of the correction approaches to correct the ocular artifact in practice. However, ICA based approach may overly or less remove the artifacts when the EEG sources and ocular sources cannot be represented in different independent components (ICs). In this paper, a new approach combining ICA and Auto-Regressive exogenous (ARX) (ICA-ARX) is proposed for a more robust removal of ocular artifact. In the proposed approach, to lower the negative effect induced by ICA, ARX is used to build the multi-models based on the ICA corrected signals and the reference EEG selected before contamination period for each channel, and then the optimal model will be selected for further artifact removal. The results applied to both the simulated signals and actual EEG recordings demonstrate the effectiveness of the proposed approach for ocular artifact removal, and its potential to be used in the EEG related studies. (C) 2013 Published by Elsevier Ltd.
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