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Status

colour	Yellow
title	app under development

Status

colour	Purple
title	Private app on bl

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

This App is supposed to be applied on MEG signals preprocessed beforehand with MaxFilter

GitHub repository

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Info
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

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Inputs of the App

Files	Format	Datatype	Description	Optional
MEG signals	.fif	meg/fif	Data to process. MaxFilter was applied beforehand on this dataThe data can be continuous or epoched. Notch filtering and resampling can have been applied beforehand.	No

Outputs of the App:

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

Steps

The python file temporal_filtering.py is composed of several functions:

Function temporal_filtering()

Apply a lowpass, highpass, or bandpass filter
Optionally apply a notch filter
Optionally resample the data
Save file

Function _compute_snr()

Computes a SNR before and after MaxFilter. The steps of that function are described here.

Function _generate_report()

Plot data in time domain
Plot data in frequency domain
Create info about raw data
Display SNR values
Save report

View file

name	report_filtering.html

Function main()

Parse the config.json
Apply temporal_filtering()
Apply _compute_snr()
Apply _generate_report()

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. They are listed in the Their default values proposed in Brainlife or in config.json.example when you test your App locally, then when you register you App on Brainlife, you enter them and a config.json is created by Brainlife.

For app-temporal-filtering, the user will be able to choose the following parameters (a default parameter will be proposed). Three different MNE Python functions are used in this App:

Function raw.filter()
- l_freq
  - NUMBER
  - default: None
- h_freq
  - NUMBER
  - default: None
- picks
  - ENUM
  - either meg, eeg, [“meg”, “eeg”], mag, grad, or None (i.e. all channels except for the bad ones)
  - default: None
- filter_length
  - STRING
  - default: auto
- l_trans_bandwidth
  - NUMBER
  - default: auto
- h_trans_bandwidth
  - NUMBER
  - default: auto
- n_jobs
  - NUMBER
  - default: 1
- method
  - STRING
  - either fir or iir
  - default: fir
- iir_params
  - to be determined
- phase
  - ENUM
  - either zero or zero-double
  - default: zero
- fir_window
  - ENUM
  - either hamming, hann, or blackman
  - default: hamming
- fir_design
  - ENUM
  - either firwin or firwin2
  - default: firwin
- skip_by_annotation
  - ENUM
  - either ["edge", "bad_acq_skip"], ["edge"], ["bad_acq_skip"], or []
- pad
  - ENUM
  - either reflect_limited or any value of numpy.pad()
  - default: reflect_limited
raw.filter_notch()
- param_notch_freqs_start
  - NUMBER
  - default: 50 (in Europe power line artifact is at 50Hz, in the US it’s at 60Hz)
- param_notch_freqs_stop
  - NUMBER
  - default: 251 (in Europe, if MEG signals were recorded in the US, change it to 251)
- param_notch_freqs_step
  - NUMBER
  - default: 50 (in Europe, if MEG signals were recorded in the US, change it to 50)
- picks
  - ENUM
  - either meg, eeg, [“meg”, “eeg”], mag, grad, or None (i.e. all channels except for the bad ones)
  - default: None
- filter_length
  - STRING
  - default: auto
- notch_widths
  - NUMBER
  - default: None
- trans_bandwidth
  - NUMBER
  - default: 1
- n_jobs
  - NUMBER
  - default: 1
- method
  - STRING
  - either fir or iir
  - default: fir
- iir_params
  - to be determined
- mt_bandwidth
  - NUMBER
  - default: None
- p_value
  - NUMBER
  - default: 0.05
- phase
  - ENUM
  - either zero or zero-double
  - default: zero
- fir_window
  - ENUM
  - either hamming, hann, or blackman
  - default: hamming
- fir_design
  - ENUM
  - either firwin or firwin2
  - default: firwin
- pad
  - ENUM
  - either reflect_limited or any value of numpy.pad()
  - default: reflect_limited
raw.resample()
- sfreq
  - NUMBER
- npad
  - NUMBER
  - default: auto
- window
  - ENUM
  - see choices in scipy.signal.get_window
  - default: boxcar
- stim_picks
  - STRING
  - default: None
- n_jobs
  - NUMBER
  - default: 1
- events
  - NUMBER
  - default: None
- pad
  - ENUM
  - either reflect_limited or any value of numpy.pad()
  - default: reflect_limited

17 Mar 2021

See if it’s possible in BL to put a default STRING value in a NUMBER parameter
iir_params must be a dictionary, to see how to write it for BL
see how to write an array of floats in BL
the list of parameters will be updated when the App will be registered on BL

NoteThe parameters' list along with their default values correspond to the 0.22.0 version of MNE. correspond to the default values of MNE Python 0.23.

Note
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?

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