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 |
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 app (see Parameters conversion to make an App run on BL).
Next improvements
Be able to use the
iir_params: register this parameter as aSTRINGin BL and convert it into a Python dict intemporal_filtering.pyAllow user to change the plot parameters for the plots in the HTML report?
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