Analysis of brain electrical topography by spatio-temporal wavelet decomposition

Currently, the Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and Electroencephalography (EEG) recordings are the major techniques of neuroimaging. The EEG with its highest temporal resolution is still a crucial measurement for localization of activities arising from the electrical behaviour of the brain. A scalp topographic map for an EEG may be a superposition of several simpler subtopographic maps, each resulting from an individual electrical source located at a certain depth. Furthermore, this source may have a temporal characteristic as an oscillation or a rhythm that extends in a certain time window which has been a basis of assumption for the time-frequency analysis methods. A method for the spatio-temporal wavelet decomposition of multichannel EEG data is proposed which facilitates the localization of electrical sources separate and/or overlapping on a continuum of time, frequency and space domains. The subtopographic maps asociated with each of these individual components are then used in the MUSIC source localization algorithm. The validations are performed on simulated EEG data. Spatio-temporal wavelet decomposition as a preprocessing method improves the source localization by simplifying the topographic data formed by the superposition of EEG generators, having possible combinations of temporal, frequency and/or spatial overlappings. Spatio-temporal analysis of EEG will help enhance the accuracy of dipole source reconstruction in neuroimaging.