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

• Function temporal_filtering()

1. Apply a lowpass, highpass, or bandpass filter

2. Optionally apply a notch filter

3. Optionally resample the data

4. Save file

• Function _compute_snr()

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

• Function _generate_report()

1. Plot data in time domain

2. Plot data in frequency domain

3. Display Create info about raw data

4. Display SNR values

5. Save report

• Function main()

1. Parse the config.json

2. Apply temporal_filtering()

3. Apply _compute_snr()

4. Apply _generate_report()

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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

    • ENUM

    • either np.arange(60, 241, 60) in the US, or np.arange(50, 251, 50) in EuropeNUMBER

    • 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_banwidthbandwidth

    • 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

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