Multi-way Space-Time-WaveVector analysis for EEG source separationReportar como inadecuado

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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 LTSI - Laboratoire Traitement du Signal et de l-Image 3 CRL - Communications Research Laboratory

Abstract : For the source analysis of ElectroEncephaloGraphic EEG data, both equivalent dipole models and more realistic distributed source models are employed. Several authors have shown that the Canonical Decomposition or PARAFAC of Space-Time-Frequency STF data can be used to fit equivalent dipoles to the electric potential data. In this paper we propose a new multi-way approach based on Space-Time-Wave-Vector STWV data obtained by a 3D local Fourier transform over space accomplished on the measured data. This method can be seen as a preprocessing step that separates the sources, reduces noise as well as interference and extracts the source time signals. The results can further be used to localize either equivalent dipoles or distributed sources increasing the performance of conventional source localization techniques like, for example, LORETA. Moreover, we propose a new, iterative source localization algorithm, called Binary Coefficient Matching Pursuit BCMP, which is based on a realistic distributed source model. Computer simulations are used to examine the performance of the STWV analysis in comparison to the STF technique for equivalent dipole fitting and to evaluate the efficiency of the STWV approach in combination with LORETA and BCMP, which leads to better results in case of the considered distributed source scenarios.

Keywords : Space-Time-Frequency- Space-Time-Wave-Vector analysis LORETA Tensor decomposition Source localization EEG PARAFAC

Autor: Hanna Becker - Pierre Comon - Laurent Albera - Martin Haardt - Isabelle Merlet -



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