Atomic Decomposition of EEG for Mapping Cortical Activation

To improve the measurement and dierentiation of normal and abnormal brain function we are developing new methods to decom- pose multichannel (electroencephalogram) EEG into elemental compo- nents or \atoms." We estimate EEG atoms using multiway analysis, specically parallel factor analysis or PARAFAC for modeling. Acti- vation sequences of EEG atoms can identify functional brain networks dynamically, with much ner time resolution than fMRI. For example, EEG atoms activate in specic combinations during the sequential op- erations of brain networks, such as Default Mode, Somatomotor, Dorsal Attention and others. Guided by the score values of the identied atoms we inferred the volumetric brain sources of the selected networks using the sLORETA pseudoinverse algorithm. To conrm network identities, we compared 2-D and 3-D functional network maps derived from EEG atoms to known functional neuroanatomy of the networks. We nd that multichannel EEGs in most individuals can be accounted for by a set of ve 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.