Extraction of P300 Using Constrained Independent Component Analysis

A brain computer interface (BCI) uses electrophysiological activities of the brain such as natural rhythms and evoked potentials to communicate with some external devices. P300 is a positive evoked potential (EP), elicited approximately 300ms after an attended external stimulus. A P300-based BCI uses this evoked potential as a means of communication with the external devices. Until now this P300-based BCI has been rather slow, as it is difficult to detect a P300 response without averaging over a number of trials. Previously, independent component analysis (ICA) has been used in the extraction of P300. However, the drawback of ICA is that it extracts not only P300 but also non-P300 related components requiring a proper selection of P300 ICs by the system. In this study we propose an algorithm based on constrained independent component analysis (cICA) for P300 extraction which can extract only the relevant component by incorporating a priori information. A reference signal is generated as this a priori information of P300 and cICA is applied to extract the P300 related component. Then the extracted P300 IC is segmented, averaged, and classified into target and non-target events by means of a linear classifier. The method is fast, reliable, computationally inexpensive as compared to ICA and achieves an accuracy of 98.3% in the detection of P300.