Tensor Decomposition of Human Oscillatory EEG Activity in Frequency, Space and Time

Abstract

Changing physiological conditions in the central nervous system are associated with excitation and inhibition of cortical neuronal sources, many of which are reflected in modulation of narrowband scalp‐ recorded EEG oscillations (NSEOs). NSEOs exhibit specific electric field patterns on the scalp, which are largely determined by the geometry of the underlying cortical sources. Isolating NSEOs using spectral and spatial filters has led to many useful applications, from understanding mechanisms of drug action, to deeper understanding of sensory, perceptual, and cognitive functions. However, the scalp-recorded EEG combines signals of multiple NSEOs and massively distributed broadband cortical sources, which in turn greatly limits the practical utility of NSEOs.Over the past 10 years we have been developing methods to improve the measurement of NSEOs using tensor decompositions such as parallel factor analysis (PARAFAC). We and others have shown that PARAFAC can accurately model NSEO activity as a tensor product of dimensions of frequency, space and time. We introduced frequency and spatial constraints, which have improved the physiological plausibility of the NSEO models. In this paper we demonstrate the principle of the tensor approach using simulated scalp EEG data obtained by forward modeling. This allows us to carefully manipulate the spectral, spatial and temporal attributes of NSEOs and validate the obtained solutions. We observe superior performance of the tensor approach when compared with spatio-spectral decomposition, a broadly used technique for measuring oscillatory activity. This is achieved without a priori narrowband filtering, which is inappropriate when isolating and measuring NSEOs with unknown spectral properties.