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Conference Paper: Multiscale entropy analysis of attention ralated EEG based on motor imaginary potential

TitleMultiscale entropy analysis of attention ralated EEG based on motor imaginary potential
Authors
KeywordsAttention
Eeg
Motor Imaginary
Multiscale Entropy
Issue Date2009
Citation
2009 Ieee International Conference On Computational Intelligence For Measurement Systems And Applications, Cimsa 2009, 2009, p. 24-27 How to Cite?
AbstractIn China, there are approximate 1.3% to 13.4% of children who have Attention Deficit Hyperactivity Disorder (ADHD), which may affect their physiology and psychology development badly. Attention related electroencephalograph (EEG) signals during the limbs motor imagery can be used to tell the different levels of people's attention. Such an EEG-based attention level discrimination can provide a method in curing ADHD and it can also be used in curing Altheimer's Disease patients. The conventional methods purpose the feature extraction of limbs motor imagery. In this study, Multiscale Entropy (MSE) is introduced to discriminate the EEG signals recorded during three attention tasks. We have discriminated the different attention states by using this method, with 63.158% accuracy to some subjects. The effectiveness of the method is proved by our experiment. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/173421
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorMing, Den_US
dc.contributor.authorZhang, Men_US
dc.contributor.authorXi, Yen_US
dc.contributor.authorQi, Hen_US
dc.contributor.authorHu, Yen_US
dc.contributor.authorLuk, KDKen_US
dc.date.accessioned2012-10-30T06:31:01Z-
dc.date.available2012-10-30T06:31:01Z-
dc.date.issued2009en_US
dc.identifier.citation2009 Ieee International Conference On Computational Intelligence For Measurement Systems And Applications, Cimsa 2009, 2009, p. 24-27en_US
dc.identifier.isbn978-1-4244-3819-8-
dc.identifier.issn2159-1547-
dc.identifier.urihttp://hdl.handle.net/10722/173421-
dc.description.abstractIn China, there are approximate 1.3% to 13.4% of children who have Attention Deficit Hyperactivity Disorder (ADHD), which may affect their physiology and psychology development badly. Attention related electroencephalograph (EEG) signals during the limbs motor imagery can be used to tell the different levels of people's attention. Such an EEG-based attention level discrimination can provide a method in curing ADHD and it can also be used in curing Altheimer's Disease patients. The conventional methods purpose the feature extraction of limbs motor imagery. In this study, Multiscale Entropy (MSE) is introduced to discriminate the EEG signals recorded during three attention tasks. We have discriminated the different attention states by using this method, with 63.158% accuracy to some subjects. The effectiveness of the method is proved by our experiment. © 2009 IEEE.en_US
dc.languageengen_US
dc.relation.ispartof2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2009en_US
dc.subjectAttentionen_US
dc.subjectEegen_US
dc.subjectMotor Imaginaryen_US
dc.subjectMultiscale Entropyen_US
dc.titleMultiscale entropy analysis of attention ralated EEG based on motor imaginary potentialen_US
dc.typeConference_Paperen_US
dc.identifier.emailHu, Y:yhud@hku.hken_US
dc.identifier.emailLuk, KDK:hcm21000@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.identifier.authorityLuk, KDK=rp00333en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/CIMSA.2009.5069911en_US
dc.identifier.scopuseid_2-s2.0-77950847811en_US
dc.identifier.hkuros159909-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77950847811&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage24en_US
dc.identifier.epage27en_US
dc.identifier.scopusauthoridMing, D=9745824400en_US
dc.identifier.scopusauthoridZhang, M=8271300800en_US
dc.identifier.scopusauthoridXi, Y=35792940000en_US
dc.identifier.scopusauthoridQi, H=7202348852en_US
dc.identifier.scopusauthoridHu, Y=7407116091en_US
dc.identifier.scopusauthoridLuk, KDK=7201921573en_US

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