Tensor-Based Preprocessing of Combined EEG-MEG DataReport as inadecuate

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* Corresponding author 1 Laboratoire d-Informatique, Signaux, et Systèmes de Sophia-Antipolis I3S - Equipe SIGNAL SIS - Signal, Images et Systèmes 2 GIPSA-CICS - CICS GIPSA-DIS - Département Images et Signal 3 LTSI - Laboratoire Traitement du Signal et de l-Image

Abstract : Due to their good temporal resolution, electroencephalography EEG and magnetoencephalography MEG are two often used techniques for brain source analysis. In order to improve the results of source localization algorithms applied to EEG or MEG data, tensor-based preprocessing techniques can be used to separate the sources and reduce the noise. These methods are based on the Canonical Polyadic CP decomposition also called Parafac of space-time-frequency STF or space-time-wave-vector STWV data. In this paper, we analyze the combination of EEG and MEG data to enhance the performance of the tensor-based preprocessing. To this end, we consider the joint CP decomposition of two or more third order tensors with one or two identical loading matrices. We present the necessary modifications for several classical CP decomposition algorithms and examine the gain on performance in the EEG-MEG context by means of simulations.

keyword : Biomedical STF STVW Parafac EEG MEG Tensor Canonical Polyadic Decomposition

Author: Hanna Becker - Pierre Comon - Laurent Albera -

Source: https://hal.archives-ouvertes.fr/


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