Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88447
Title: Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis
Authors: Li, Xinyang
Guan, Cuntai
Zhang, Haihong
Ang, Kai Keng
Keywords: Brain–computer Interface
Electroencephalogram
Issue Date: 2016
Source: Li, X., Guan, C., Zhang, H., & Ang, K. K. (2017). Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis. IEEE Transactions on Biomedical Engineering, 64(8), 1906-1913.
Series/Report no.: IEEE Transactions on Biomedical Engineering
Abstract: Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain-computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for independent component analysis based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis. Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real-world EEG dataset comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.
URI: https://hdl.handle.net/10356/88447
http://hdl.handle.net/10220/44617
ISSN: 0018-9294
DOI: 10.1109/TBME.2016.2628958
Schools: School of Computer Science and Engineering 
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TBME.2016.2628958].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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