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:
- gradient artifact removal
- ballistocardigram artifact removal
The following features may be added in the future:
- Residual noise removal using ICA
- EEG motion detection and censoring
- Source localization of cleaned scalp space signal
- Co-registration of source space with FMRI voxel space
- Frequency specific EEG-FMRI correlations in each voxel
- Time-varying heart rate based on frequency of EEG ballistocardiogram artifacts
- Respiratory volume based on amplitude of EEG ballistocardiogram artifacts
- Millisecond precise head motion amplitude based on broadband high frequency EEG power
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