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MI-BCI

MI-BCI means minimal invasive brain-computer interface (BCI). The repository is the sample code for the paper "Intracranial brain-computer interface spelling using localized visual motion response."

Content

Overview

The code demonstrates the basic analysis pipeline of this paper, including fMRI localization and visualization, and SEEG processing.

The fMRI localizer:

The activated MT area is obtained for choosing the optimal implantation spots.

The BCI paradigm:

System Requirements

This package required the following softwares to function properly:

  • FreeSurfer 7.1.1
  • Brainnetome atlas freesurfer
  • python 3.6 or higher

Installation Guide

Python

Note!!! Installation using pip may face a problem of dependency package version conflict! The newest 'mayavi' version has a conflict with the newest 'vtk' package, so in requirement.txt we define the downloading versions to be 'vtk==8.1.2' and 'mayavi==4.6.2'. If you have installed a later version of mayavi or vtk before, please notice the version changing issue.

 git clone https://github.com/HongLabTHU/MI-BCI.git
 cd MI-BCI
 pip install -r requirements.txt

Brainnetome atlas

  1. Download the atlas from the official website.
  2. Copy the files lh.BN_Atlas.gcs, rh.BN_Atlas.gcs, BN_Atlas_subcortex.gca, BN_Atlas_246_LUT.txt, to the FreeSurfer SUBJECTS_DIR folder.

Tutorial

MRI Preprocessing

To run this demo, you need to recon the surface using your local FreeSurfer:

cd ./MT-Localizer
./recon.sh

Wait until the recon-all command has finished. Then, run fsfast_mt_analysis.sh to analyze fMRI

./fsfast_mt_analysis.sh -p ../data/fsfast --all

Then, perform parcellation based on Brainnetome atlas:

./BN_atlas_mapping.sh -s S1

Electrode mark

Enter module localizer-viz

cd ..
cd ./localizer-viz

Project the marked electrodes to the pial or inflated surface.

python elecs_projection.py -s S1 --fsfast ../data/fsfast --hemi lh -eloc ../data/fsfast/AnatDir/S1_Electrode_locations.dat
# for detailed explaination of the input arguments, please type ``python elecs_projection.py --help ``

Now, we can visualize the result

python viz.py -s S1 --fsfast ../data/fsfast --hemi lh -eloc ../data/fsfast/AnatDir/S1_Electrode_locations.dat

SEEG processing

cd ..
cd SEEG-proc
python seeg_processing.py

ERP waveforms:

HG power:

Example Dataset

For example data, please click here to download. Unzip the file and put the two folders SEEG and fsfast into the data directory.

Support

If you have a question or feedback, or find any problems, please feel free to open a git issue or pull request. Thanks!