Rosipal R., Trejo L.J., Zaidel E.
ML: Tensor Methods for Machine Learning, ECML/PKDD 2013 Workshop, Prague, Czech Republic, 2013.
To improve the measurement and differentiation of normal and abnormal brain function we are developing new methods to decompose multichannel (electroencephalogram) EEG into elemental components or ''atoms.'' We estimate EEG atoms using multiway analysis, specifically parallel factor analysis or PARAFAC for modeling. Activation sequences of EEG atoms can identify functional brain networks dynamically, with much finer time resolution than fMRI. For example, EEG atoms activate in specific combinations during the sequential operations of brain networks, such as Default Mode, Somatomotor, Dorsal Attention and others. Guided by the score values of the identified atoms we inferred the volumetric brain sources of the selected networks using the sLORETA pseudoinverse algorithm. To confirm network identities, we compared 2-D and 3-D functional network maps derived from EEG atoms to known functional neuroanatomy of the networks. We find that multichannel EEGs in most individuals can be accounted for by a set of five to six standard atoms, which parallel classical EEG bands, and have unique power spectra, scalp and cortical topographies. We discuss how we may use the activation sequences of these atoms to describe the dynamic interplay of functional brain networks.