Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface
dc.contributor.author | Zhou, Shang-Ming | |
dc.contributor.author | Gan, JQ | |
dc.contributor.author | Sepulveda, F | |
dc.date.accessioned | 2023-02-15T13:30:59Z | |
dc.date.available | 2023-02-15T13:30:59Z | |
dc.date.issued | 2008-03 | |
dc.identifier.issn | 0020-0255 | |
dc.identifier.issn | 1872-6291 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/20376 | |
dc.description.abstract |
In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate. © 2007 Elsevier Inc. All rights reserved. | |
dc.format.extent | 1629-1640 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.subject | brain-computer interfaces | |
dc.subject | classification | |
dc.subject | electroencephalogram (EEG) | |
dc.subject | feature extraction | |
dc.subject | higher-order statistics | |
dc.subject | bispectrum | |
dc.title | Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000253660400013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 6 | |
plymouth.volume | 178 | |
plymouth.publication-status | Published | |
plymouth.journal | Information Sciences | |
dc.identifier.doi | 10.1016/j.ins.2007.11.012 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Health | |
plymouth.organisational-group | /Plymouth/Faculty of Health/School of Nursing and Midwifery | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.identifier.eissn | 1872-6291 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1016/j.ins.2007.11.012 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |