dc.contributor.author | Divakar, Purushothaman | |
dc.contributor.author | Paulraj, Murugesa Pandiyan, Prof. Madya Dr. | |
dc.contributor.author | Abdul Hamid, Adom, Dr. | |
dc.contributor.author | Hema, Chengalvarayan Radhakrishnamurthy | |
dc.date.accessioned | 2012-10-21T08:06:40Z | |
dc.date.available | 2012-10-21T08:06:40Z | |
dc.date.issued | 2010-10-16 | |
dc.identifier.uri | http://dspace.unimap.edu.my/123456789/21494 | |
dc.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. | en_US |
dc.description.abstract | 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%. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universiti Malaysia Perlis (UniMAP) | en_US |
dc.relation.ispartofseries | Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010) | en_US |
dc.subject | Brain machine interface | en_US |
dc.subject | Colour visual tasks | en_US |
dc.subject | Neural network | en_US |
dc.title | BMI using spectral energy entropy for colour visual tasks | en_US |
dc.type | Working Paper | en_US |
dc.publisher.department | Centre for Graduate Studies | en_US |
dc.contributor.url | divakaar@gmail.com | en_US |