NOTE: This python code works, but it's sub-optimal for a number of reasons (Python-specific issues, and MNE-specific issues). We've implemented it in Matlab with much better results: https://github.com/gmierz/eegfmri-matlab
Python code for denoising eeg data from eeg-fmri experiments, and fusing eeg-fmri data.
A fully automatic pipeline for cleaning raw EEG data acquired inside the scanner.
The package performs the following in sequential order:
The following features may be added in the future:
NB: due to overhead, the maximum size EEG dataset for a computer with 16 Gb RAM is 1.5Gb, this will typically only be a problem during the gradient denoising step
Dependencies can be installed with
cd eegfmripy
python setup.py install
These dependencies are the same as for mne-python (https://github.com/mne-tools/mne-python/blob/master/README.rst).
After installation, you can run eegfmripy --help
for more info. The pipelines available can be found in the clianalysis folder. Use the configuration YAML's in the configs folder for options that can be supplied to each script.
Example for the full clean - gradient artifact removal, followed by BCG artifact removal:
eegfmripy clean -c configs/config_clean.yml