01333nas a2200145   4500008004100000245004900041210004800090260001200138520093100150100002001081700002501101700001301126700001801139856003001157       2006                        eng d00aRegularised CSP for Sensor Selection in BCI.0 aRegularised CSP for Sensor Selection in BCI  c01/20063 a<p>The Common Spatial Pattern (CSP) algorithm is a highly successful method for efficiently calculating spatial filters for brain signal classification. Spatial filtering can improve classification performance considerably, but demands that a large number of electrodes be mounted, which is inconvenient in day-to-day BCI usage. The CSP algorithm is also known for its tendency to overfit, i.e. to learn the noise in the training set rather than the signal. Both problems motivate an approach in which spatial filters are sparsified. We briefly sketch a reformulation of the problem which allows us to do this, using 1-norm regularisation. Focusing on the electrode selection issue, we present preliminary results on EEG data sets that suggest that effective spatial filters may be computed with as few as 10–20 electrodes, hence offering the potential to simplify the practical realisation of BCI systems significantly.</p>1 aFarquhar, Jason1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchölkopf, B  uhttp://edoc.mpg.de/312060