ffrprep.preproc.filter_data

ffrprep.preproc.filter_data(eeg_data=None, high_pass=None, low_pass=None, filter_method='fir')[source]

Apply frequency filters to the provided EEG data object.

Parameters:

  • eeg_data (MNE data object) – MNE data object containing EEG data and metadata.

  • high_pass (float) – Lower passband edge (in Hertz). Default = None.

  • low_pass (float) – Upper passband edge (in Hertz). Default = None.

  • filter_method ({"fir", "iir"}) – "fir" (default) uses MNE’s zero-phase FIR design with automatic transition bandwidths. "iir" applies zero-phase (forward- backward) first-order Butterworth filters, a high-pass then a low-pass, so the band-pass rolls off at 12 dB/octave overall. This mirrors the butter/filtfilt pipeline used in some published FFR analyses.

Returns:

filtered_eeg – EEG data filtered to given frequency range.

Return type:

MNE data object

Examples

Filter an EEG data object with a band-pass filter (70-1000 Hz).

>>> filtered_data = filter_data(eeg_data, high_pass=70.0, low_pass=1000.0)

Filter an EEG data object with a high-pass filter (0.1 Hz).

>>> filtered_data = filter_data(eeg_data, high_pass=0.1)

Filter an EEG data object with a low-pass filter (100 Hz).

>>> filtered_data = filter_data(eeg_data, low_pass=100.0)