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dc.contributor.authorKumar, Shiu
dc.contributor.authorSharma, Alok
dc.date.accessioned2019-07-04T12:41:33Z
dc.date.available2019-07-04T12:41:33Z
dc.date.issued2018
dc.identifier.issn0140-0118
dc.identifier.doi10.1007/s11517-018-1821-4
dc.identifier.urihttp://hdl.handle.net/10072/381846
dc.description.abstractA brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.ispartofpagefrom1861
dc.relation.ispartofpageto1874
dc.relation.ispartofissue10
dc.relation.ispartofjournalMedical and Biological Engineering and Computing
dc.relation.ispartofvolume56
dc.subject.fieldofresearchBiomedical Engineering not elsewhere classified
dc.subject.fieldofresearchBiomedical Engineering
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchcode090399
dc.subject.fieldofresearchcode0903
dc.subject.fieldofresearchcode0906
dc.subject.keywordsBrain-computer interface (BCI)
dc.subject.keywordsFilter tuning
dc.subject.keywordsGenetic algorithm (GA)
dc.subject.keywordsMotor imagery (MI)
dc.subject.keywordsTemporal filters
dc.titleA new parameter tuning approach for enhanced motor imagery EEG signal classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2018 Springer Berlin / Heidelberg. This is an electronic version of an article published in Medical and Biological Engineering and Computing, Vol. 56(10):1861-1874, 2018. Medical and Biological Engineering and Computing is available online at: http://link.springer.com/ with the open URL of your article.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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