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Blind source separation of medial temporal discharges via partial dictionary learning

Sparsity is known to be very beneficial in blind source separation (BSS). Even if data is not sparse in its current domain, it can be modelled as sparse linear combinations of atoms of a chosen dictionary. The choice of dictionary that sparsifies the data is very important. In this paper the dictionary is partly pre-specified based on chirplet modelling of various kinds of real epileptic discharges, and partly learned using a dictionary learning algorithm. The dictionary which includes a fixed and a variable (i.e. learned) part, is incorporated into a source separation framework to extract the closest source to the source of interest from the mixtures. Experiments on synthetic mixtures of real data consisting of epileptic discharges are used to evaluate the proposed method, and the results are compared with a traditional BSS algorithm.