Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/20496
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dc.contributor.authorPaulraj, Murugesa Pandiyan, Assoc. Prof. Dr.-
dc.contributor.authorAbdul Hamid, Adom, Assoc. Prof. Dr.-
dc.contributor.authorHema, Chengalvarayan Radhakrishnamurthy-
dc.contributor.authorPurushothaman, Divakar-
dc.date.accessioned2012-07-19T13:47:32Z-
dc.date.available2012-07-19T13:47:32Z-
dc.date.issued2012-02-27-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/20496-
dc.descriptionInternational Conference on Man Machine Systems (ICoMMS 2012) organized by School of Mechatronic Engineering, co-organized by The Institute of Engineer, Malaysia (IEM) and Society of Engineering Education Malaysia, 27th - 28th February 2012 at Bayview Beach Resort, Penang, Malaysia.en_US
dc.description.abstractElectroencephalogram (EEG) signals are the electrophysiological measures of brain function and it is used to develop a brain machine interface. Brain machine interface (BMI) system is used to provide a communication and control technology for the mentally able people having neuromuscular disorders. In this paper, a simple BMI system based on EEG signal emanated while imagining of different colours has been proposed. The proposed BMI uses the color imagination tasks (CIT) and aims to provide a communication link using brain activated control signal; the required task operation can be then performed and the needs of the physically retarded community can be accomplished. Two feature extraction method are used for analysis namely energy and entropy. The extracted features are then associated to different control signals and a probabilistic neural network model (PNN) has been developed. The effectiveness of the two features are compared using PNN classification accuracy.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.relation.ispartofseriesProceedings of the International Conference on Man-Machine Systems (ICoMMS 2012)en_US
dc.subjectBrain machine interfaceen_US
dc.subjectColour imagination tasksen_US
dc.subjectNeural networken_US
dc.titleClassification of EEG colour imagination tasks based BMI using energy and entropy featuresen_US
dc.typeWorking Paperen_US
dc.publisher.departmentSchool of Mechatronic Engineeringen_US
dc.contributor.urlpaul@unimap.edu.myen_US
dc.contributor.urlabdhamid@unimap.edu.myen_US
Appears in Collections:Conference Papers
Abdul Hamid Adom, Prof. Dr.
Paulraj Murugesa Pandiyan, Assoc. Prof. Dr.

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