Temporal Feature Selection for Optimizing Spatial Filters in a P300 Brain-Computer Interface

For the creation of efficient and robust BrainComputer Interfaces (BCIs) based on the detection of eventrelated potentials (ERPs) in the electroencephalogram (EEG), spatial filtering has been shown as being an important step for feature extraction and reduction. Current spatial filtering methods for ERP enhancement typically consider a global approach by enhancing the signal on a predefined time-segment that contains all the different ERP components, which can have different spatial distributions. Because several ERP components occur, it is likely that they have different neural sources, and require specific signal processing methods. We propose to use a spatial filtering method based on the maximization of the signalto-signal plus noise ratio, and compare different approaches to determine the best time segment for optimizing the choice of the spatial filters. The evaluation was carried out on data recorded of ten healthy subjects during a P300 speller experiment. The results support the conclusion that spatial filters based on the global approach provide the best solution and outperform local and hybrid approaches.