Robust removal of ocular artifacts by combining Independent Component Analysis and system identification

• Published in: Biomedical signal processing and control Vol. 10; pp. 250 - 259
• Main Authors: Wang, ZhenYu, Xu, Peng, Liu, TieJun, Tian, Yin, Lei, Xu, Yao, DeZhong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2014
• Subjects: Auto-Regressive eXogenous; Electroencephalogram; Independent Component Analysis; Ocular artifact; System identification; Auto-Regressive eXogenous; System identification; Electroencephalogram; Ocular artifact; Independent Component Analysis
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2013.10.006

•The ARX model is used for ocular artifacts removal for the first time.•The distortion of EEG information can be revealed by ARX model.•ARX model can be used to remove the ocular artifacts. 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.