Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/6834
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHema, Chengalvarayan Radhakrishnamurthy-
dc.contributor.authorPaulraj, Murugesapandian-
dc.contributor.authorRamachandran, Nagarajan-
dc.contributor.authorSazali, Yaacob-
dc.contributor.authorAbdul Hamid, Adom-
dc.date.accessioned2009-08-11T08:40:00Z-
dc.date.available2009-08-11T08:40:00Z-
dc.date.issued2007-11-28-
dc.identifier.citationp.53-56en_US
dc.identifier.isbn978-0-7695-2994-1-
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?=&arnumber=4457491-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/6834-
dc.descriptionInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.description.abstractPatients with neurodegenerative diseases loose all motor movements including impairment of speech, leaving the patients totally locked-in. One possible option for rehabilitation of such patients is using a brain machine interfaces (BMI) which uses their active cognition capabilities to control external devices and their environment. BMIs are designed using the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper five different mental tasks from five subjects were studied, for classification combinations of two tasks are studied for each subject. A fuzzy based classification method is proposed for classification of the EEG mental task signals. Power of the spectral band frequencies of the EEG are used as features for training and testing the fuzzy classifier. Classification accuracies ranged from 65% to 100% for different combinations of mental tasks. The results validate the performance of the proposed algorithm for mental task classification.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofseriesProceedings of 3rd International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2007)en_US
dc.subjectElectroencephalographyen_US
dc.subjectCognitionen_US
dc.subjectSignal classificationen_US
dc.subjectUser interfacesen_US
dc.subjectBrain machine interfacesen_US
dc.titleFuzzy based classification of EEG mental tasks for a brain machine interfaceen_US
dc.typeArticleen_US
Appears in Collections:School of Mechatronic Engineering (Articles)
Sazali Yaacob, Prof. Dr.
Ramachandran, Nagarajan, Prof. Dr.
Abdul Hamid Adom, Prof. Dr.
Paulraj Murugesa Pandiyan, Assoc. Prof. Dr.

Files in This Item:
File Description SizeFormat 
Abstract8.11 kBAdobe PDFView/Open


Items in UniMAP Library Digital Repository are protected by copyright, with all rights reserved, unless otherwise indicated.