Towards a Brain Computer Interface using wavelet transform with averaged and time segmented adapted wavelets

A Brain Computer Interface (BCI) is a system that provides an individual a non-muscular way to control his surroundings by working on the activity signals generated in his brain. Activity signals collected from the brain in the form Electroencephalogram (EEG) are used in the system described in this paper. Time Domain normalization has been used in the preprocessing stage. Wavelet transform with pattern adapted wavelet filter is used for the purpose of feature extraction. Exact Radial Basis Neural networks are used for the classification of signals and heuristic weight adjustment of neural networks was done in final decision making. Tests carried out on two different datasets give accuracies of more than 91% and 86%.