mne.filter.notch_filter#
- mne.filter.notch_filter(x, Fs, freqs, filter_length='auto', notch_widths=None, trans_bandwidth=1, method='fir', iir_params=None, mt_bandwidth=None, p_value=0.05, picks=None, n_jobs=None, copy=True, phase='zero', fir_window='hamming', fir_design='firwin', pad='reflect_limited', *, verbose=None)[source]#
Notch filter for the signal x.
Applies a zero-phase notch filter to the signal x, operating on the last dimension.
- Parameters:
- x
array
Signal to filter.
- Fs
float
Sampling rate in Hz.
- freqs
float
|array
offloat
|None
Frequencies to notch filter in Hz, e.g. np.arange(60, 241, 60). None can only be used with the mode ‘spectrum_fit’, where an F test is used to find sinusoidal components.
- filter_length
str
|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 forphase="zero-double"
.int: Specified length in samples. For fir_design=”firwin”, this should not be used.
When
method=='spectrum_fit'
, this sets the effective window duration over which fits are computed. Seemne.filter.create_filter()
for options. Longer window lengths will give more stable frequency estimates, but require (potentially much) more processing and are not able to adapt as well to non-stationarities.The default in 0.21 is None, but this will change to
'10s'
in 0.22.- notch_widths
float
|array
offloat
|None
Width of the stop band (centred at each freq in freqs) in Hz. If None, freqs / 200 is used.
- trans_bandwidth
float
Width of the transition band in Hz. Only used for
method='fir'
.- method
str
‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR forward-backward filtering (via filtfilt). ‘spectrum_fit’ will use multi-taper estimation of sinusoidal components. If freqs=None and method=’spectrum_fit’, significant sinusoidal components are detected using an F test, and noted by logging.
- iir_params
dict
|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()
.- mt_bandwidth
float
|None
The bandwidth of the multitaper windowing function in Hz. Only used in ‘spectrum_fit’ mode.
- p_value
float
P-value to use in F-test thresholding to determine significant sinusoidal components to remove when method=’spectrum_fit’ and freqs=None. Note that this will be Bonferroni corrected for the number of frequencies, so large p-values may be justified.
- picks
list
|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. Only supported for 2D (n_channels, n_times) and 3D (n_epochs, n_channels, n_times) data.- n_jobs
int
|str
Number of jobs to run in parallel. Can be ‘cuda’ if
cupy
is installed properly and method=’fir’.- copy
bool
If True, a copy of x, filtered, is returned. Otherwise, it operates on x in place.
- phase
str
Phase of the filter. When
method='fir'
, symmetric linear-phase FIR filters are constructed, and ifphase='zero'
(default), the delay of this filter is compensated for, making it non-causal. Ifphase='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 constructed and applied, which is causal but has weaker stop-band suppression. Whenmethod='iir'
,phase='zero'
(default) orphase='zero-double'
constructs and applies IIR filter twice, once forward, and once backward (making it non-causal) using filtfilt. Ifphase='forward'
, it constructs and applies forward IIR filter using lfilter.New in version 0.13.
- fir_window
str
The window to use in FIR design, can be “hamming” (default), “hann” (default in 0.13), or “blackman”.
New in version 0.15.
- fir_design
str
Can be “firwin” (default) to use
scipy.signal.firwin()
, or “firwin2” to usescipy.signal.firwin2()
. “firwin” uses a time-domain design technique that generally gives improved attenuation using fewer samples than “firwin2”.New in version 0.15.
- pad
str
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'
.- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- x
- Returns:
- xf
array
The x array filtered.
- xf
See also
Notes
The frequency response is (approximately) given by:
1-|---------- ----------- | \ / |H| | \ / | \ / | \ / 0-| - | | | | | 0 Fp1 freq Fp2 Nyq
For each freq in freqs, where
Fp1 = freq - trans_bandwidth / 2
andFs2 = freq + trans_bandwidth / 2
.References
Multi-taper removal is inspired by code from the Chronux toolbox, see www.chronux.org and the book “Observed Brain Dynamics” by Partha Mitra & Hemant Bokil, Oxford University Press, New York, 2008. Please cite this in publications if method ‘spectrum_fit’ is used.