Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.sigpro.2005.10.018
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dc.titleRobust adaptive techniques for minimization of EOG artefacts from EEG signals
dc.contributor.authorPuthusserypady, S.
dc.contributor.authorRatnarajah, T.
dc.date.accessioned2014-10-07T04:35:53Z
dc.date.available2014-10-07T04:35:53Z
dc.date.issued2006-09
dc.identifier.citationPuthusserypady, S., Ratnarajah, T. (2006-09). Robust adaptive techniques for minimization of EOG artefacts from EEG signals. Signal Processing 86 (9) : 2351-2363. ScholarBank@NUS Repository. https://doi.org/10.1016/j.sigpro.2005.10.018
dc.identifier.issn01651684
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/82987
dc.description.abstractIn this paper, we propose the application of H∞ techniques for minimization of electrooculogram (EOG) artefacts from corrupted electroencephalographic (EEG) signals. Two adaptive algorithms (time-varying and exponentially-weighted) based on the H∞ principles are proposed. The idea of applying H∞ techniques is motivated by the fact that they are robust to model uncertainties and lack of statistical information with respect to noise [B. Hassibi, A.H. Sayed, T. Kailath, Linear estimation in Krein spaces-Part 1: theory & Part II: applications, IEEE Trans. Automat. Control 41 (1996) 18-49]. Studies are performed on simulated as well as real recorded signals. Performance of the proposed techniques are then compared with the well-known least-mean square (LMS) and recursive least-square (RLS) algorithms. Improvements in the output signal-to-noise ratio (SNR) along with the time plots are used as criteria for comparing the performance of the algorithms. It is found that the proposed H∞-based algorithms work slightly better than the RLS algorithm (especially when the input SNR is very low) and always outperform the LMS algorithm in minimizing the EOG artefacts from corrupted EEG signals. © 2005 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.sigpro.2005.10.018
dc.sourceScopus
dc.subjectElectroencephalogram (EEG)
dc.subjectElectrooculogram (EOG) artefacts
dc.subjectRobust adaptive filtering
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.sigpro.2005.10.018
dc.description.sourcetitleSignal Processing
dc.description.volume86
dc.description.issue9
dc.description.page2351-2363
dc.description.codenSPROD
dc.identifier.isiut000239256300020
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