Recognition and classification of P300s in EEG signals by means of feature extraction using wavelet decomposition

In the last twenty years the understanding of the brain function and the advent of powerful low-cost computer equipment allowed the birth and the development of the BCI (brain-computer interface), a device that interprets brain activity to issue commands. P300 is a positive peak at about 300 ms from a stimulus, and has been used as a base for a BCI in many studies. The aim of this research consists in recognizing and classifying P300 signals by using wavelet transforms. This study analyzes both the kind of wavelets and which coefficients are more suited for a 100% correct decisions using as few repetitions of stimuli as possible. The classifier performs a quadratic discriminant analysis. The method is tested on the ldquoBCI Competition 2003rdquo data set IIb with excellent results.