dc.contributor.authorLi, Xinyang
dc.contributor.authorGuan, Cuntai
dc.contributor.authorZhang, Haihong
dc.contributor.authorAng, Kai Keng
dc.date.accessioned2018-03-26T08:46:44Z
dc.date.available2018-03-26T08:46:44Z
dc.date.issued2016
dc.identifier.citationLi, 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.en_US
dc.identifier.issn0018-9294en_US
dc.identifier.urihttp://hdl.handle.net/10220/44617
dc.description.abstractElectrooculogram (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.en_US
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)en_US
dc.format.extent8 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE Transactions on Biomedical Engineeringen_US
dc.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].en_US
dc.subjectBrain–computer Interfaceen_US
dc.subjectElectroencephalogramen_US
dc.titleDiscriminative Ocular Artifact Correction for Feature Learning in EEG Analysisen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TBME.2016.2628958
dc.description.versionAccepted versionen_US


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