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Detection algorithms for electroencephalography (EEG) data typically employ handcrafted features that take advantage of the signal's specific properties. In the field of interictal epileptic discharge (IED) detection, the feature representation that provides optimal classification performance is still an unresolved issue. In this paper, we consider deep learning for automatic feature generation from...
Electroencephalography (EEG) signals arise as mixtures of various neural processes which occur in particular spatial, frequency, and temporal brain locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes;...
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...
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,...
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