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dc.contributor.authorHema, Chengalvarayan Radhakrishnamurthy
dc.contributor.authorPaulraj, Murugesapandian
dc.contributor.authorSazali, Yaacob
dc.contributor.authorAbdul Hamid, Adom
dc.contributor.authorRamachandran, Nagarajan
dc.date.accessioned2009-11-18T07:33:11Z
dc.date.available2009-11-18T07:33:11Z
dc.date.issued2009-10-11
dc.identifier.citationp.5B1 1 - 5B1 5en_US
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/7339
dc.descriptionOrganized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.en_US
dc.description.abstractBrain Machine Interface (BMI) provides a digital link between the brain and a device such as a computer, robot or wheelchair. This paper presents a BMI design using Neuro-Fuzzy classifiers for controlling a wheelchair using EEG signals. EEG signals during motor imagery (MI) of left and right hand movements are recorded noninvasively at the sensorimotor cortex. Four mental task signals are analyzed and classified to design a four class BMI. The proposed classifier has an average classification performance of 97%.en_US
dc.description.sponsorshipTechnical sponsored by IEEE Malaysia Sectionen_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlisen_US
dc.relation.ispartofseriesProceedings of the International Conference on Man-Machine Systems (ICoMMS 2009)en_US
dc.subjectBrain Machine Interfacesen_US
dc.subjectEEG motor imageryen_US
dc.subjectEEG band poweren_US
dc.subjectNeuro-fuzzy classifiersen_US
dc.subjectBrain-computer interfacesen_US
dc.subjectComputational neuroscienceen_US
dc.subjectBioengineeringen_US
dc.titleNeuro-Fuzzy based motor imagery classification for a four class brain machine interfaceen_US
dc.typeWorking Paperen_US
dc.contributor.urlhema@unimap.edu.myen_US


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