app-resampling

This is a draft of a future Brainlife App that resamples MEG signals using the MNE functions: mne.io.Raw.resample or mne.Epochs.resample.

app-resampling documentation

1. Resample MEG signals
2. Input file is:
   • a MEG file in .fif format,
   • an optional fine calibration file in .dat,
   • an optional crosstalk compensation file in .fif,
   • an optional head position file in .pos,
   • an optional destination file in .fif,
   • an optional events file in .tsv,
   • an optional channels file in .tsv.
3. Input parameters are:
   • param_epoched_data: bool, if True, the data to be resampled is epoched, else it is continuous.
   • param_sfreq: float, new sample rate to use.
   • param_npad: int or str, amount to pad the start and end of the data. Default is 'auto'.
   • param_window: str, frequency-domain window to use in resampling. Default is boxcar.
   • param_stim_picks: list of int or None, stim channels. Default is None.
   • param_n_jobs: int or str, number of jobs to run in parallel. Can be 'cuda' if cupy is installed properly. Default is 1.
   • param_raw_pad: str, the type of padding to use for raw data. Default is 'reflect_limited'.
   • param_epoch_pad: str, the type of padding to use for epoched data. Default is 'edge'.
   • param_save_jointly_resampled_events: bool, if True, save the events file resampled jointly with the data. Default is True.
   • param_pick_type: str or None, select meg or eeg channels. If None all channels are selected. Default is None.

This list along with the parameters' default values correspond to the 0.22.0 version of MNE Python.

5. Ouput files are:
   • a .fif MEG file after resampling,
   • an optional .tsv events file with the resampled events.

Authors

• Aurore Bussalb

Contributors

• Aurore Bussalb
• Maximilien Chaumon

Funding Acknowledgement

brainlife.io is publicly funded and for the sustainability of the project it is helpful to Acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your code and publications. Copy and past the following lines into your repository when using this code.

Citations

1. Avesani, P., McPherson, B., Hayashi, S. et al. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 6, 69 (2019). https://doi.org/10.1038/s41597-019-0073-y
2. Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A., & Jas, M. MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software, 4:1896 (2019). https://doi.org/10.21105/joss.01896

Running the App

On Brainlife.io

This App has not yet been registered in Brainlife.io.

Running Locally (on your machine)

1. git clone this repo
2. Inside the cloned directory, create config.json with the same keys as in config.json.example but with paths to your input files and values of the input parameters. For instance:

{
  "fif": "rest1-raw.fif"
}

3. Launch the App by executing main

./main

Output

The output files are a MEG file in .fif format and an optional .tsv events file.