Selecting relevant electrode positions for classification tasks based on the electro-encephalogram

The aim is to describe a general approach to determining important electrod e positions when measured electro-encephalogram signals are used for classi fication. The approach is exemplified in the frame of the brain-computer in terface, which crucially depends on the classification of different brain s tates. To classify two brain states, e.g. planning of movement of right and left index fingers, three different approaches are compared: classificatio n using a physiologically motivated set of four electrodes, a set determine d by principal component analysis and electrodes determined by spatial patt ern analysis. Spatial pattern analysis enhances the classification rate sig nificantly from 61.3 +/- 1.8% (with four electrodes) to 71.8 +/- 1.4%, wher eas the classification rate using principal component analysis is significa ntly lower (65.2 +/- 1.4%). Most of the 61 electrodes used have no influenc e on the classification rate, so that, in future experiments, the setup can be simplified drastically to six to eight electrodes without loss of infor mation.