Classification of colour imagination tasks using spectral energy entropy

Electroencephalography (EEG) is the measure of brain waves used to monitor the health of the patient in medical applications and other research areas. EEG signals can also be used to develop a brain machine interface. Brain machine interface (BMI) is an interfacing system, helps 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 and aims to provide a communication link using brain activated control signal. Using this thought controlled signals, the required operation or task can be performed and the needs of the physically retarded community can be accomplished. From the recorded EEG signal of colour imagination tasks, the spectral analysis has been made and the alpha, beta and gamma band frequency spectrum features using energy entropy are obtained. The extracted feature set is then fed to the neural network model to classify the different tasks in BMI. Probabilistic neural network model has been developed and the classification results are analyzed in this paper.