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Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering.

Abstract : The reconstruction of brain sources from non-invasive electroencephalography (EEG) or magnetoencephalography (MEG) via source imaging can be distorted by information redundancy in case of high-resolution recordings. Dimensionality reduction approaches such as spatial projection may be used to alleviate this problem. In this proof-of-principle paper we apply spatial projection to solve the problem of information redundancy in case of source reconstruction via spatiotemporal Kalman filtering (STKF), which is based on state-space modeling. We compare two approaches for incorporating spatial projection into the STKF algorithm and select the best approach based on its performance in source localization with respect to accurate estimation of source location, lack of spurious sources, computational speed and small number of required optimization steps in state-space model parameter estimation. We use state-of-the-art simulated EEG data based on neuronal population models, for which the number and location of sources is known, to validate the source reconstruction results of the STKF. The incorporation of spatial projection into the STKF algorithm solved the problem of information redundancy, resulting in correct source localization with no spurious sources, and decreased the overall computational time in STKF analysis. The results help make STKF analyses of high-density EEG, MEG or simultaneous MEG-EEG data more feasible.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01624623
Contributor : Lotfi Senhadji <>
Submitted on : Thursday, October 26, 2017 - 3:33:07 PM
Last modification on : Wednesday, August 19, 2020 - 12:08:19 PM

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Laith Hamid, Ali Al Farawn, Isabelle Merlet, Natia Japaridze, Ulrich Heute, et al.. Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering.. 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), 2017, pp.2213-2217. ⟨10.1109/EMBC.2017.8037294⟩. ⟨hal-01624623⟩

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