mne.filter.create_filter#

mne.filter.create_filter(data, sfreq, l_freq, h_freq, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', method='fir', iir_params=None, phase='zero', fir_window='hamming', fir_design='firwin', verbose=None)[source]#

Create a FIR or IIR filter.

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

Parameters

datandarray, shape (…, n_times) | None

The data that will be filtered. This is used for sanity checking only. If None, no sanity checking related to the length of the signal relative to the filter order will be performed.

sfreqfloat

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.

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'.

methodstr

‘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 is None and method=”iir”, 4th order Butterworth will be used. For more information, see mne.filter.construct_iir_filter().

phasestr

Phase of the filter, only used if method='fir'. Symmetric linear-phase FIR filters are constructed, and if phase='zero' (default), the delay of this filter is compensated for, making it non-causal. If phase='zero-double', then this filter is applied twice, once forward, and once backward (also making it non-causal). If 'minimum', then a minimum-phase filter will be constricted and applied, which is causal but has weaker stop-band suppression.

New in version 0.13.

fir_windowstr

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

New in version 0.15.

fir_designstr

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 version 0.15.

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.

Returns

filtarray or dict: Will be an array of FIR coefficients for method=’fir’, and dict with IIR parameters for method=’iir’.

See also

filter_data

Notes

Note

For FIR filters, the cutoff frequency, i.e. the -6 dB point, is in the middle of the transition band (when using phase=’zero’ and fir_design=’firwin’). For IIR filters, the cutoff frequency is given by l_freq or h_freq directly, and l_trans_bandwidth and h_trans_bandwidth are ignored.

Band-pass filter

The frequency response is (approximately) given by:

  1-|               ----------
    |             /|         | \
|H| |            / |         |  \
    |           /  |         |   \
    |          /   |         |    \
  0-|----------    |         |     --------------
    |         |    |         |     |            |
    0        Fs1  Fp1       Fp2   Fs2          Nyq

Where:

Fs1 = Fp1 - l_trans_bandwidth in Hz

Fs2 = Fp2 + h_trans_bandwidth in Hz

Band-stop filter

The frequency response is (approximately) given by:

  1-|---------                   ----------
    |         \                 /
|H| |          \               /
    |           \             /
    |            \           /
  0-|             -----------
    |        |    |         |    |        |
    0       Fp1  Fs1       Fs2  Fp2      Nyq

Where Fs1 = Fp1 + l_trans_bandwidth and Fs2 = Fp2 - h_trans_bandwidth.

Multiple stop bands can be specified using arrays.

Low-pass filter

The frequency response is (approximately) given by:

  1-|------------------------
    |                        \
|H| |                         \
    |                          \
    |                           \
  0-|                            ----------------
    |                       |    |              |
    0                      Fp  Fstop           Nyq

Where Fstop = Fp + trans_bandwidth.

High-pass filter

The frequency response is (approximately) given by:

  1-|             -----------------------
    |            /
|H| |           /
    |          /
    |         /
  0-|---------
    |        |    |                     |
    0      Fstop  Fp                   Nyq

Where Fstop = Fp - trans_bandwidth.

New in version 0.14.

Examples using `mne.filter.create_filter`#

Background information on filtering

Filtering and resampling data

mne.filter.construct_iir_filter

mne.filter.estimate_ringing_samples

mne.filter.create_filter#

Examples using mne.filter.create_filter#

Examples using `mne.filter.create_filter`#