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Article: Feature exaction and classification of attention related electroencephalographic signals based on sample entropy

TitleFeature exaction and classification of attention related electroencephalographic signals based on sample entropy
Authors
KeywordsBiofeedback
Electroencephalography
Sample entropy
Support vector machine
Issue Date2007
PublisherXi'an Jiaotong Daxue.
Citation
Hsi-An Chiao Tung Ta Hsueh/Journal Of Xi'an Jiaotong University, 2007, v. 41 n. 10, p. 1237-1241 How to Cite?
AbstractA method regarding the sample entropy (SampEn) as features is proposed to carry out the analysis and classification of attention related electroencephalographic(EEG) signals, and the support vector machine (SVM) algorithm is used as classifiers for classification, seven males (aged from 20 to 30) are recruited to perform three attention-related tasks, including attention, inattention, and relaxation states. The processing results demonstrate that the classification accuracy of the SampEn gets up to 85.5% for classifying the relation between attention and inattention, obviously much higher than that with frequency band power (77.9%). It indicates that the SampEn is more effective to extract the information attention-related in EEG to show the clinical application prospects in EEG biofeedback systems.
Persistent Identifierhttp://hdl.handle.net/10722/128523
ISSN
2015 SCImago Journal Rankings: 0.246
References

 

DC FieldValueLanguage
dc.contributor.authorYan, Nen_HK
dc.contributor.authorWang, Jen_HK
dc.contributor.authorWei, Nen_HK
dc.contributor.authorZong, Len_HK
dc.date.accessioned2010-11-01T07:09:46Z-
dc.date.available2010-11-01T07:09:46Z-
dc.date.issued2007en_HK
dc.identifier.citationHsi-An Chiao Tung Ta Hsueh/Journal Of Xi'an Jiaotong University, 2007, v. 41 n. 10, p. 1237-1241en_HK
dc.identifier.issn0253-987Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/128523-
dc.description.abstractA method regarding the sample entropy (SampEn) as features is proposed to carry out the analysis and classification of attention related electroencephalographic(EEG) signals, and the support vector machine (SVM) algorithm is used as classifiers for classification, seven males (aged from 20 to 30) are recruited to perform three attention-related tasks, including attention, inattention, and relaxation states. The processing results demonstrate that the classification accuracy of the SampEn gets up to 85.5% for classifying the relation between attention and inattention, obviously much higher than that with frequency band power (77.9%). It indicates that the SampEn is more effective to extract the information attention-related in EEG to show the clinical application prospects in EEG biofeedback systems.en_HK
dc.languageeng-
dc.publisherXi'an Jiaotong Daxue.-
dc.relation.ispartofHsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong Universityen_HK
dc.subjectBiofeedbacken_HK
dc.subjectElectroencephalographyen_HK
dc.subjectSample entropyen_HK
dc.subjectSupport vector machineen_HK
dc.titleFeature exaction and classification of attention related electroencephalographic signals based on sample entropyen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0253-987X&volume=41&issue=10&spage=1237&epage=1241&date=2007&atitle=Feature+exaction+and+classification+of+attention+related+electroencephalographic+signals+based+on+sample+entropy-
dc.identifier.emailYan, N: nyan@hku.hken_HK
dc.identifier.authorityYan, N=rp00978en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.scopuseid_2-s2.0-36248939315en_HK
dc.identifier.hkuros183217-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-36248939315&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume41en_HK
dc.identifier.issue10en_HK
dc.identifier.spage1237en_HK
dc.identifier.epage1241en_HK
dc.identifier.scopusauthoridYan, N=7102919410en_HK
dc.identifier.scopusauthoridWang, J=23991822700en_HK
dc.identifier.scopusauthoridWei, N=55108058000en_HK
dc.identifier.scopusauthoridZong, L=36767777800en_HK

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