Quadratic B-Spline Wavelet and Committee Machine for the P300 Detection in Brain Computer Interface

In this study we propose a new approach to analyze data from the P300 speller paradigm using the quadratic B-Spline wavelet coefficients in comparing to time and frequency features sets on the event related potentials. Data set II from the BCI competition 2005 was used. Mode frequency, Mean frequency, Median frequency and some morphologic parameters ware extracted as features. Three methods were used for comparing three feature subsets, first Davies Bouldin criteria, correlation based method and classification accuracy criteria. For all criteria, best result was extracted from wavelet coefficients, at the final wavelet coefficients were used as inputs into committee machines (CM) based on LDA, MLP and SVM. This algorithm achieved an accuracy of 97.6% for train data and 94.2% for test data of subject A in target and non target detection also accuracy of 98.2% for train data and 92.8% for test data of subject B.