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dc.contributor.authorDivakar, Purushothaman
dc.contributor.authorPaulraj, Murugesa Pandiyan, Prof. Madya Dr.
dc.contributor.authorAbdul Hamid, Adom, Dr.
dc.contributor.authorHema, Chengalvarayan Radhakrishnamurthy
dc.date.accessioned2012-10-21T08:06:40Z
dc.date.available2012-10-21T08:06:40Z
dc.date.issued2010-10-16
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/21494
dc.descriptionInternational Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.en_US
dc.description.abstractEEG signals are the electrophysiological measures of brain function and it is used to develop a Brain machine Interface. A Brain machine Interface (BMI) system is used to provide a communication and control technology for the people having severe neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke, quadriplegics and spinal cord injury. In this paper, a simple BMI system based on EEG signal emanated while visualizing of different colours has been proposed. The proposed BMI uses the color visual tasks and aims to provide a communication through brain activated control signal for a system from which the required task operation can be performed to accomplish the needs of the physically retarded community. The ability of an individual to control his EEG through the colour visualization enables him to control devices. Using spectral analysis, the alpha, beta and gamma band frequency spectrum features using energy entropy are obtained for each EEG signals. The extracted features are then associated to different control signals and a neural network model using probabilistic neural network (PNN) has been developed. The proposed method can be used to translate the colour visualization signals into control signals and used to control the movement of a mobile robot. The performance of the proposed algorithm has an average classification accuracy of 96.23%.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.relation.ispartofseriesProceedings of the International Postgraduate Conference on Engineering (IPCE 2010)en_US
dc.subjectBrain machine interfaceen_US
dc.subjectColour visual tasksen_US
dc.subjectNeural networken_US
dc.titleBMI using spectral energy entropy for colour visual tasksen_US
dc.typeWorking Paperen_US
dc.publisher.departmentCentre for Graduate Studiesen_US
dc.contributor.urldivakaar@gmail.comen_US


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