P300 component detection by using Weighted Common Temporal Pattern

Detection of Event Related Potentials (ERP) is an important prerequisite in the ERP-based Brain-Computer Interface (BCI) systems. In order to increase the classification accuracy in these systems, different filtering methods are used for improving the signal to noise ratio. This improvement facilitates the diagnosis and classification of the ERPs. In a number of studies, the performance of P300 detection systems which are based on common spatial pattern (CSP) and common temporal pattern (CTP) has been investigated. The former uses spatial filters while the latter is based on temporal filters. In these methods the filters are trained such that they maximize variance of one class and simultaneously minimize the other class variance. The associated results show that in P300 speller systems, the temporal filters outperform the spatial filters. In this study, in order to improve the performance of the CTP based systems, a Weighted Common Temporal Pattern (WCTP) algorithm which is a combined method is proposed. In this algorithm, each category of features has a weight based on the importance of its eigenvalues. In fact, the features produced by the initial and final CTP filters have more weight in the decision making process. In the combined method used in this algorithm, the LDA classifiers are used. It is shown that the set of features obtained by the WCTP method leads to an average classification accuracy of 90.2 percent which is about 4 percent better than the CTP method. The experiments are performed considering two different subjects on 5 trials.