Multiview classification of brain data through tensor factorisation

Brain signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In detection paradigms, algorithms are developed that target specific processes. In this work, we apply tensor factorisation to a set of intracranial electroencephalography data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures that are common between the subjects. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components. These classifiers are then combined in a leave-subject-out fashion and subsequently used to estimate the classification accuracy of each combination on left-out subjects' data. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made.