mne.filter.notch_filter(x, Fs, freqs, filter_length='', notch_widths=None, trans_bandwidth=1, method='fir', iir_params=None, mt_bandwidth=None, p_value=0.05, picks=None, n_jobs=1, copy=True, phase='', fir_window='', verbose=None)

Notch filter for the signal x.

Applies a zero-phase notch filter to the signal x, operating on the last dimension.


x : array

Signal to filter.

Fs : float

Sampling rate in Hz.

freqs : float | array of float | 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):

  • int: specified length in samples.
  • ‘auto’ (default in 0.14): 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’).
  • str: (default in 0.13 is “10s”) 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".

notch_widths : float | array of float | 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. See mne.filter.construct_iir_filter for details. If iir_params is None and method=”iir”, 4th order Butterworth will be used.

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 : array-like of int | None

Indices of channels to filter. If None all channels will be filtered. 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 scikits.cuda is installed properly, CUDA is initialized, 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, only used if method='fir'. By default, a symmetric linear-phase FIR filter is constructed. If phase='zero' (default in 0.14), the delay of this filter is compensated for. If phase=='zero-double' (default in 0.13 and before), then this filter is applied twice, once forward, and once backward.

New in version 0.13.

fir_window : str

The window to use in FIR design, can be “hamming” (default in 0.14), “hann” (default in 0.13), or “blackman”.

New in version 0.13.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).


xf : array

x filtered.

See also

filter_data, band_pass_filter, band_stop_filter, high_pass_filter, low_pass_filter, resample


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 and Fs2 = freq + trans_bandwidth / 2.


Multi-taper removal is inspired by code from the Chronux toolbox, see 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.

Examples using mne.filter.notch_filter