Classification of EEG colour imagination tasks based BMI using energy and entropy features
Date
2012-02-27Author
Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.
Abdul Hamid, Adom, Assoc. Prof. Dr.
Hema, Chengalvarayan Radhakrishnamurthy
Purushothaman, Divakar
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Show full item recordAbstract
Electroencephalogram (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.