Multivariate Autoregressive Model Constrained by Anatomical Connectivity to Reconstruct Focal Sources

Abstract : In this paper, we present a framework to reconstruct spatially localized sources from Magnetoencephalogra-phy (MEG)/Electroencephalography (EEG) using spatiotempo-ral constraint. The source dynamics are represented by a Mul-tivariate Autoregressive (MAR) model whose matrix elements are constrained by the anatomical connectivity obtained from diffusion Magnetic Resonance Imaging (dMRI). The framework assumes that the whole brain dynamic follows a constant MAR model in a time window of interest. The source activations and the MAR model parameters are estimated iteratively. We could confirm the accuracy of the framework using simulation experiments in both high and low noise levels. The proposed framework outperforms the two-stage approach.
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01357167
Contributor : Brahim Belaoucha <>
Submitted on : Monday, August 29, 2016 - 1:04:58 PM
Last modification on : Friday, July 20, 2018 - 2:56:02 PM
Long-term archiving on: Wednesday, November 30, 2016 - 1:20:18 PM

File

root.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

  • HAL Id : hal-01357167, version 1

Collections

Citation

Brahim Belaoucha, Mouloud Kachouane, Théodore Papadopoulo. Multivariate Autoregressive Model Constrained by Anatomical Connectivity to Reconstruct Focal Sources. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Aug 2016, Orlando, United States. ⟨hal-01357167⟩

Share

Metrics

Record views

549

Files downloads

277