braindecode.preprocessing.SavgolFilter#

class braindecode.preprocessing.SavgolFilter(h_freq, verbose=None)[source]#

Braindecode preprocessor wrapper for savgol_filter().

Filter the data using Savitzky-Golay polynomial method.

Parameters:
h_freqfloat

Approximate high cut-off frequency in Hz. Note that this is not an exact cutoff, since Savitzky-Golay filtering [1] is done using polynomial fits instead of FIR/IIR filtering. This parameter is thus used to determine the length of the window over which a 5th-order polynomial smoothing is used.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
instinstance of Epochs, Evoked or SourceEstimate

The object with the filtering applied.

See also

mne.io.Raw.filter

Notes

For Savitzky-Golay low-pass approximation, see:

When working on SourceEstimates the sample rate of the original data is inferred from tstep.

Added in version 0.9.0.

Examples

>>> import mne
>>> from os import path as op
>>> evoked_fname = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample', 'sample_audvis-ave.fif')
>>> evoked = mne.read_evokeds(evoked_fname, baseline=(None, 0))[0]
>>> evoked.savgol_filter(10.)  # low-pass at around 10 Hz
>>> evoked.plot()
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