Page Comparison

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• Function main()

1. Parse the config.json

2. Raise an exception if both l_freq and h_freq are None

3. Apply temporal_filtering()

4. Apply _compute_snr()

5. 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:

1. Function raw.filter()

• l_freq

  • NUMBER

  • default: None

• h_freq

  • NUMBER

  • default: None

Some parameters are in the “Advanced” section because it’s best to keep their default values:

• 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

2. Function 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)60)

Some parameters are in the “Advanced” section because it’s best to keep their default values:

• 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

3. Function 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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