Design of EEG Based Wheel Chair by Using Color Stimuli and Rhythm Analysis

A novel methodology of brain controlled intelligent wheelchair by using color stimuli is proposed here. A general methodology is applied to find out most effective rhythm for color classification. Primary colors RGB and secondary color yellow were chosen for left, right, forward and stop command. Alpha, Beta, Theta, Delta rhythms were analyzed for three different subjects. Using dissimilar features of time and frequency domain twelve artificial neural network were built to decide the best rhythm. Principal component analysis was made for each EEG signal to remove the background effect of color stimuli. Comparing the findings it is visualized that beta rhythm is the most efficient rhythm with minimum mean square error of 4.845×10^-9 among all designed ANN for color classification.