Spatial filtering in the training process of a brain computer interface

The spatial filtering of electroencephalogram data is crucial when analyzing brain activity. Spatial filters increase the signal-to-noise ratio, thus allowing better classification of the analyzed mental states. This study shows the evolution in the selection of the most appropriate spatial filter when subjects are training to control a brain-computer interface. Different filters - the common average reference and the estimation of the surface Laplacian both using finite different methods and spherical splines - have been adapted and evaluated for a particular configuration of electrodes, using only eight positions: F₃, C₃, P₃, C_z, P_z, F₄, C₄, and P₄.