app-temporal-filtering

APP UNDER DEVELOPMENT PRIVATE APP ON BL

This App aims at apply to MEG signals temporal filtering (lowpass, highpass, or bandpass) on epoched or continuous data. To do so, the MNE Python functions raw.filter and Epoch.filter are used.

The filter function is the same for epoched and raw data but the value of the pad parameter is not the same if the data is continuous or epoched.

GitHub repository

https://github.com/AuroreBussalb/app-temporal-filtering

Brainlife datatype used:

For input: neuro/meg/fif
For outputs: neuro/meg/fif and report/html

Inputs of the App

Files	Format	Description	Optional
MEG signals	.fif	The data can be continuous or epoched. Notch filtering and resampling can have been applied beforehand.	No

Outputs of the App:

Files	Format	Description
MEG signals	.fif	Data after filtering
Report	.html	Visualization in time and frequency domains

Besides, in the output directory, there are the optional files that can be used in a next App (see How to run Apps one after another).

Parameters of the App

The parameters of the App correspond to the parameters passed to the Python functions used in the App. Their default values proposed in Brainlife or in config.json.example correspond to the default values of MNE Python 0.23.

The parameter iir_params can’t be used on BL (it’s set to “READ ONLY”) because it is a dictionary and BL doesn’t handle Python dictionaries as parameter values and no conversion script has been written in the python file of this app (see Parameters conversion to make an App run on BL).

Next improvements

Be able to use the iir_params: register this parameter as a STRING in BL and convert it into a Python dict in temporal_filtering.py
Allow user to change the plot parameters for the plots in the HTML report?