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Brain-Computer Interface: Common Tensor Discriminant Analysis Classifier Evaluation
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SYSNO ASEP 0365748 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Brain-Computer Interface: Common Tensor Discriminant Analysis Classifier Evaluation Tvůrce(i) Frolov, A. (RU)
Húsek, Dušan (UIVT-O) RID, SAI, ORCID
Bobrov, P. (CZ)Zdroj.dok. Nature and Biologically Inspired Computing. - Piscataway : IEEE, 2011 / Abraham A. ; Corchado E. ; Berwick R. ; de Carvalho A. ; Zomaya A. ; Yager R. - ISBN 978-1-4577-1122-0 Rozsah stran s. 614-620 Poč.str. 7 s. Akce NaBIC 2011. World Congress on Nature and Biologically Inspired Computing /3./ Datum konání 19.10.2011-21.10.2011 Místo konání Salamanca Země ES - Španělsko Typ akce WRD Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova human computer interface ; motor imagery ; EEG signal classification ; Bayesian classification ; Common Spatial Patterns ; Common Tensor Discriminant Analysis Vědní obor RIV IN - Informatika CEP GAP202/10/0262 GA ČR - Grantová agentura ČR GA205/09/1079 GA ČR - Grantová agentura ČR 1M0567 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy CEZ AV0Z10300504 - UIVT-O (2005-2011) EID SCOPUS 83755173780 DOI 10.1109/NaBIC.2011.6089732 Anotace The performance of the Common Tensor Discriminant Analysis CTDA method for Brain-Computer Interface EEG pattern classification is compared with three other classifiers. The classifiers are designed with the aim to distinguish EEG patterns appearing as a result of performance of several mental tasks. Classifier comparison has yielded quite similar results as regards our experimental imagery movement data set as well as for BCI Competition IV data set. The Bayesian and Multiclass Common Spatial Patterns classifiers, which use solely interchannel covariance as input, are shown to be comparable in performance, while lagging behind the Multiclass Common Spatial Patterns classifier and the CTDA classifier, that is classifiers which additionally account for EEG frequency structure. It is shown that the CTDA classifier and the Multiclass Common Spatial Patterns classifier provide significantly better classification than other two methods but at a higher computational cost. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2012
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