ABSTRACT
We examine the question, whether machine-learning classifiers can be used to differentiate between experimental conditions on the basis of brain signals. The degree to which it is possible to perform such separation would provide information about how far the conditions are away from each other. We introduce the very recently developed classifier Maximum Contrast Classifiers (MCC) (Meinicke et al., 2003) for analysis of EEG data and compare them with results obtained using the well-established Support Vector Machines (SVM) (Vapnik, 1995).
