mne.filter.filter_data(data, sfreq, l_freq, h_freq, picks=None, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=None, method='fir', iir_params=None, copy=True, phase='zero', fir_window='hamming', fir_design='firwin', pad='reflect_limited', *, verbose=None)[source]#

Filter a subset of channels.

datandarray, shape (…, n_times)

The data to filter.


The sample frequency in Hz.

l_freqfloat | None

For FIR filters, the lower pass-band edge; for IIR filters, the lower cutoff frequency. If None the data are only low-passed.

h_freqfloat | None

For FIR filters, the upper pass-band edge; for IIR filters, the upper cutoff frequency. If None the data are only high-passed.

pickslist | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. None (default) will pick all channels. Note that channels in info['bads'] will be included if their indices are explicitly provided. Currently this is only supported for 2D (n_channels, n_times) and 3D (n_epochs, n_channels, n_times) arrays.

filter_lengthstr | int

Length of the FIR filter to use (if applicable):

  • ‘auto’ (default): The filter length is chosen based on the size of the transition regions (6.6 times the reciprocal of the shortest transition band for fir_window=’hamming’ and fir_design=”firwin2”, and half that for “firwin”).

  • str: A human-readable time in units of “s” or “ms” (e.g., “10s” or “5500ms”) will be converted to that number of samples if phase="zero", or the shortest power-of-two length at least that duration for phase="zero-double".

  • int: Specified length in samples. For fir_design=”firwin”, this should not be used.

l_trans_bandwidthfloat | str

Width of the transition band at the low cut-off frequency in Hz (high pass or cutoff 1 in bandpass). Can be “auto” (default) to use a multiple of l_freq:

min(max(l_freq * 0.25, 2), l_freq)

Only used for method='fir'.

h_trans_bandwidthfloat | str

Width of the transition band at the high cut-off frequency in Hz (low pass or cutoff 2 in bandpass). Can be “auto” (default in 0.14) to use a multiple of h_freq:

min(max(h_freq * 0.25, 2.), info['sfreq'] / 2. - h_freq)

Only used for method='fir'.

n_jobsint | str

Number of jobs to run in parallel. Can be 'cuda' if cupy is installed properly and method='fir'.


'fir' will use overlap-add FIR filtering, 'iir' will use IIR forward-backward filtering (via filtfilt()).

iir_paramsdict | None

Dictionary of parameters to use for IIR filtering. If iir_params=None and method="iir", 4th order Butterworth will be used. For more information, see mne.filter.construct_iir_filter().


If True, a copy of x, filtered, is returned. Otherwise, it operates on x in place.


Phase of the filter. When method='fir', symmetric linear-phase FIR filters are constructed with the following behaviors when method="fir":

"zero" (default)

The delay of this filter is compensated for, making it non-causal.


A minimum-phase filter will be constructed by decomposing the zero-phase filter into a minimum-phase and all-pass systems, and then retaining only the minimum-phase system (of the same length as the original zero-phase filter) via scipy.signal.minimum_phase().


This is a legacy option for compatibility with MNE <= 0.13. The filter is applied twice, once forward, and once backward (also making it non-causal).


This is a legacy option for compatibility with MNE <= 1.6. A minimum-phase filter will be reconstructed from the zero-phase filter with half the length of the original filter.

When method='iir', phase='zero' (default) or equivalently 'zero-double' constructs and applies IIR filter twice, once forward, and once backward (making it non-causal) using filtfilt(); phase='forward' will apply the filter once in the forward (causal) direction using lfilter().

New in v0.13.

Changed in version 1.7: The behavior for phase="minimum" was fixed to use a filter of the requested length and improved suppression.


The window to use in FIR design, can be “hamming” (default), “hann” (default in 0.13), or “blackman”.

New in v0.15.


Can be “firwin” (default) to use scipy.signal.firwin(), or “firwin2” to use scipy.signal.firwin2(). “firwin” uses a time-domain design technique that generally gives improved attenuation using fewer samples than “firwin2”.

New in v0.15.


The type of padding to use. Supports all numpy.pad() mode options. Can also be "reflect_limited", which pads with a reflected version of each vector mirrored on the first and last values of the vector, followed by zeros. Only used for method='fir'. The default is 'reflect_limited'.

New in v0.15.

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.

datandarray, shape (…, n_times)

The filtered data.


Applies a zero-phase low-pass, high-pass, band-pass, or band-stop filter to the channels selected by picks.

l_freq and h_freq are the frequencies below which and above which, respectively, to filter out of the data. Thus the uses are:

  • l_freq < h_freq: band-pass filter

  • l_freq > h_freq: band-stop filter

  • l_freq is not None and h_freq is None: high-pass filter

  • l_freq is None and h_freq is not None: low-pass filter


If n_jobs > 1, more memory is required as len(picks) * n_times additional time points need to be temporarily stored in memory.

For more information, see the tutorials Background information on filtering and Filtering and resampling data and mne.filter.create_filter().