Abstract
Analysis of changes in the brain neural electrical activity measured by the electroencephalogram (EEG)
plays a crucial role in the area of brain disorder diagnostics. The elementary latent sources of the
brain neural activity can be extracted by a tensor decomposition of continuously recorded multichannel
EEG. Parallel factor analysis (PARAFAC) is a powerful approach for this purpose. However, the assumption
of the same number of factors in each dimension of the PARAFAC model may be restrictive when applied to
EEG data. In this article we discuss the potential benefits of an alternative tensor decomposition
method - the Tucker model. We analyze situations, where in comparison to the PARAFAC solution, the
Tucker model provides a more parsimonious representation of the EEG data decomposition. We show that
this more parsimonious representation of EEG is achieved without reducing the ability to explain
variance. We analyze EEG records of two patients after ischemic stroke and we focus on the extraction
of specific sensorimotor oscillatory sources associated with motor imagery during neurorehabilitation
training. Both models provided consistent results. The advantage of the Tucker model was a compact
structure with only two spatial signatures reflecting the expected lateralized activation of the
detected subject-specific sensorimotor rhythms.
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