A Comparison of Non-negative Tucker Decomposition and Parallel Factor Analysis for Identification and Measurement of Human EEG Rhythms

Abstract

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.