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This App aims at apply to MEG signals temporal filtering (lowpass, highpass, or bandpass), and optionally a notch filter and a resampling. To do so, the MNE Python functions raw.filter, raw.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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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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1. Function raw.filter()

• l_freq

  • NUMBER

    • optional

    • default:

    None

    • 0.5

• h_freq

  • NUMBER

    • optional

    • default:

    None

    • 150

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

• picks

  • ENUMSTRING

    • either meg, eeg, [“meg”, “eeg”], mag, grad, a list of channels names, or None (i.e. all channels except for the bad ones)

    • default: None

• filter_length

  • STRING

    • default: auto

• l_trans_bandwidth

  • NUMBERSTRING

    • default: auto

    • Read only

• h_trans_bandwidth

  • NUMBERSTRING

    • default: auto

    • Read only

• n_jobs

  • NUMBER

    • default: 1

• method

  • STRINGENUM

    • either fir or iir

    • default: fir

• iir_params

  to be determined

  • STRING

    • optional

    • default: None

• 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_apply_notch

  • BOOLEAN

    • default: True

• 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 241)

• param_notch_freqs_step

  • NUMBER

    • default: 50 (in Europe, if MEG signals were recorded in the US, change it to 60)

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

• picks

  • ENUM

    STRING 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

    • optional

    • default: None

• trans_bandwidth

  • NUMBER

    • default: 1

• n_jobs

  • NUMBER

    • default: 1

• method

  • STRINGENUM

    • either fir or iir

    • default: fir

• iir_params

  to be determined

  • STRING

    • optional

    • default: None

• 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()

• apply_resample

  • BOOLEAN

    • Default: False

• sfreq

  • NUMBER

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

• npad

  • NUMBERSTRING

    • Read only

    • default: auto

• window

  • ENUM

    • see choices in scipy.signal.get_window

    • default: boxcar

• stim_picks

  • STRING

    • optional

    • default: None

• n_jobs

  • NUMBER

    • default: 1

• events

  • NUMBER

    • optional

    • default: None

• pad

  • ENUM

    • either reflect_limited or any value of numpy.pad()

    • default: reflect_limited

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