Tensor factorization to extract patterns in multimodal EEG data

Noisy multi-way data sets are ubiquitous in many domains. In neuroscience, electroencephalogram (EEG) data are recorded during periodic stimulation from different sensory modalities, leading to steady-state (SS) recordings with at least four ways: the channels, the time, the subjects and the modalities. Improving the signal-to-noise ratio (SNR) of the SS responses is crucial to enable their practical use. Supervised spatial filtering methods can be considered for this purpose to relevantly guide the extraction of specific activity patterns. Nevertheless, such approaches are difficult to validate with few subjects and can process at most two data ways simultaneously, the remaining ones being either averaged or considered independently despite their dependencies. This paper hence designs unsupervised tensor factorization models to enable identifying meaningful underlying structures characterized in all ways of multimodal SS data. We show on EEG recordings from 15 subjects that such factorizations faithfully reveal consistent spatial topographies, time courses with enhanced SNR and subject variations of the periodic brain activity