Comparison Between Linear and Tensor Models of EEG Signals Representation

Electroencephalography (EEG) is an important toolfor the study of the human brain because it provides potentiallyuseful signals for understanding the spatial and temporal dynam-ics of neural information processing. These signals are commonlyrepresented by vector or matrix mathematical structures, whichmay counteract their natural behaviour for a multidimensionalrepresentation. Thus, in this case, the information from an EEGsignal should be represented using tensors. This study presentsan analysis of how these different mathematical structures canbe explored to obtain functional brain information. Two matrixmodels and one tensor model were investigated and assessed usingbrain maps and classification results. Our results show at leastthree different and complementary ways for the representationof cognitive brain maps and, as far as our exploratory analysis isconcerned, the tensorial model stands out in terms of the highestlevel of compression and precision in comparison to the othermodels.