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项目作者： BBELAOUCHA

项目描述 ：
A solver of EEG/MEG inverse problem using a multivariate auto-regressive model on the source space
高级语言： Jupyter Notebook
项目地址: git://github.com/BBELAOUCHA/iSDR_p.git
创建时间： 2017-08-28T15:05:29Z
项目社区：https://github.com/BBELAOUCHA/iSDR_p
开源协议：
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iSDR_p (iterative source and dynamics reconstruction)

A solver of EEG/MEG inverse problem using a multivariate auto-regressive model (MVAR) on the source space

picture

iSDR_p is a C++/Python package for solving the EEG/MEG inverse problem using structural/functional prior
on the causality between brain regions/sources.
iSDR_p solve the following functional:

to obtain the brain activity with an initial MAR model A_i’s. Then, brain regions/sources interactions are obtained by optimizing the following cost function:

Where: 

  1.  * A_i: i=1,..,p are the matrices of the MVAR model of order p.
  3.  * M_t: EEG or/and MEG measurements at time t.
  5.  * G: Lead field matrix which project brain activity into sensor space.
  7.  * J_{t-i}: Brain activity (distributed source model with fixed position) at time t-i.
  9.  * alpha ]0,100[: percentage of the maximum alpha_max (reg parameter which results in zero brain activity).

Requirements

1-MATIO > 1.5.2
2-HDF5
3-FLENS
4-MKL intel
5-CMake

Cite

(1) Brahim Belaoucha, Théodore Papadopoulo. Large brain effective network
from EEG/MEG data and dMR information. PRNI 2017 – 7th International
Workshop on Pattern Recognition in NeuroImaging, Jun 2017, Toronto, Canada.

(2) 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. 2016.

Author

Belaoucha Brahim
Theodore Papadopoulo

Installation

Assuming your are in the source directory
mkdir build
cmake -DMKL_ROOT_DIR=YOUR_MKL_INSTALL_DIR -Dmatio_DIR=YOUR_MATIO_INSTALL_DIR -DFLENS_INCLUDE_DIR=YOUR_FLENS_INSTALL_DIR ..
make

Details

More details about the package input parameters can be obtained by:

iSDR_p —help (-h).

The first argument of ./iSDR_p is a .mat file containing the following:

  1. * M: measurements.
  3. * G: Lead field matrix.
  5. * GA: lead field matrix times initial values of A_i's, i=1,..,p i.e. GA = [GA1,..,GAp].
  7. * SC: structural connectivity matrix (symmetric).
  9. * n_c: size of sensor space.
  11. * n_t: number of measurements samples.
  13. * n_s: size of source space.
  15. * m_p: order of MVAR.

Example

An example of iSDR can be seeing in the examples folder.

Test

To test the iSDR_p package, you can run the test module:

test_MxNE_iSDR

To use it in Python

Now, it is possible to use iSDR in Python to reconstruct the brain activity. Go to ./python_wrapper for more details on how to use the package on python