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Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.

Christian Bénar 1, * Théodore Papadopoulo 2 Maureen Clerc 2
* Corresponding author
2 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : We present a method for decomposing MEG or EEG data (channel x time x trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both low-frequency evoked potentials and high-frequency oscillatory activity. We show that the method is able to recover well both low-frequency and high-frequency simulated activities. There was however cross-talk across some recovered components due to the correlation introduced in the simulation.
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Submitted on : Thursday, November 22, 2007 - 5:05:34 PM
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Christian Bénar, Théodore Papadopoulo, Maureen Clerc. Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.. Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2007, 1, pp.5461-4. ⟨10.1109/IEMBS.2007.4353581⟩. ⟨inserm-00189947⟩



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