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dc.contributor.authorKumar, S
dc.contributor.authorSharma, A
dc.contributor.authorTsunoda, T
dc.date.accessioned2020-04-14T05:50:44Z
dc.date.available2020-04-14T05:50:44Z
dc.date.issued2019
dc.identifier.isbn9783030299101
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-29911-8_55
dc.identifier.urihttp://hdl.handle.net/10072/393003
dc.description.abstractOver the last decade, processing of biomedical signals using machine learning algorithms has gained widespread attention. Amongst these, one of the most important signals is electroencephalography (EEG) signal that is used to monitor the brain activities. Brain-computer-interface (BCI) has also become a hot topic of research where EEG signals are usually acquired using non-invasive sensors. In this work, we propose a scheme based on common spatial spectral pattern (CSSP) and optimization of temporal filters for improved motor imagery (MI) EEG signal recognition. CSSP is proposed as it improves the spatial resolution while the temporal filter is optimized for each subject as the frequency band which contains most significant information varies amongst different subjects. The proposed scheme is evaluated using two publicly available datasets: BCI competition III dataset IVa and BCI competition IV dataset 1. The proposed scheme obtained promising results and outperformed other state-of-the-art methods. The findings of this work will be beneficial for developing improved BCI systems.
dc.description.peerreviewedYes
dc.publisherSpringer
dc.relation.ispartofconferencename16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019)
dc.relation.ispartofconferencetitleLecture Notes in Computer Science
dc.relation.ispartofdatefrom2019-08-26
dc.relation.ispartofdateto2019-08-30
dc.relation.ispartoflocationCuvu, Fiji
dc.relation.ispartofpagefrom712
dc.relation.ispartofpageto722
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.ispartofvolume11671
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.titleSubject-Specific-Frequency-Band for Motor Imagery EEG Signal Recognition Based on Common Spatial Spectral Pattern
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationKumar, S; Sharma, A; Tsunoda, T, Subject-Specific-Frequency-Band for Motor Imagery EEG Signal Recognition Based on Common Spatial Spectral Pattern, Lecture Notes in Computer Science, 2019, 11671, pp. 712-722
dc.date.updated2020-04-06T23:36:07Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© Springer Nature Switzerland AG 2019. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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