BMI using spectral energy entropy for colour visual tasks
International 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.
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Universiti Malaysia Perlis (UniMAP)
2012
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| Online Access: | http://dspace.unimap.edu.my/xmlui/handle/123456789/21494 |
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my.unimap-214942012-10-21T08:06:40Z BMI using spectral energy entropy for colour visual tasks Divakar, Purushothaman Paulraj, Murugesa Pandiyan, Prof. Madya Dr. Abdul Hamid, Adom, Dr. Hema, Chengalvarayan Radhakrishnamurthy divakaar@gmail.com Brain machine interface Colour visual tasks Neural network International 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. EEG 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%. 2012-10-21T08:06:40Z 2012-10-21T08:06:40Z 2010-10-16 Working Paper http://hdl.handle.net/123456789/21494 en Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010) Universiti Malaysia Perlis (UniMAP) Centre for Graduate Studies |
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Universiti Malaysia Perlis |
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| language |
English |
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Brain machine interface Colour visual tasks Neural network |
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Brain machine interface Colour visual tasks Neural network Divakar, Purushothaman Paulraj, Murugesa Pandiyan, Prof. Madya Dr. Abdul Hamid, Adom, Dr. Hema, Chengalvarayan Radhakrishnamurthy BMI using spectral energy entropy for colour visual tasks |
| description |
International 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. |
| author2 |
divakaar@gmail.com |
| author_facet |
divakaar@gmail.com Divakar, Purushothaman Paulraj, Murugesa Pandiyan, Prof. Madya Dr. Abdul Hamid, Adom, Dr. Hema, Chengalvarayan Radhakrishnamurthy |
| format |
Working Paper |
| author |
Divakar, Purushothaman Paulraj, Murugesa Pandiyan, Prof. Madya Dr. Abdul Hamid, Adom, Dr. Hema, Chengalvarayan Radhakrishnamurthy |
| author_sort |
Divakar, Purushothaman |
| title |
BMI using spectral energy entropy for colour visual tasks |
| title_short |
BMI using spectral energy entropy for colour visual tasks |
| title_full |
BMI using spectral energy entropy for colour visual tasks |
| title_fullStr |
BMI using spectral energy entropy for colour visual tasks |
| title_full_unstemmed |
BMI using spectral energy entropy for colour visual tasks |
| title_sort |
bmi using spectral energy entropy for colour visual tasks |
| publisher |
Universiti Malaysia Perlis (UniMAP) |
| publishDate |
2012 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/21494 |
| _version_ |
1643793408033030144 |