mne.decoding.FilterEstimator#

class mne.decoding.FilterEstimator(info, 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, fir_design='firwin', *, verbose=None)[source]#

Estimator to filter RtEpochs.

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, h_freq is None: low-pass filter

l_freq is None, h_freq is not None: high-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.

Parameters:

infomne.Info

The mne.Info object with information about the sensors and methods of measurement.

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.

picksstr | array_like | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

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’.

methodstr

‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR filtering.

iir_paramsdict | 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.

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.

See also

TemporalFilter

Notes

This is primarily meant for use in conjunction with mne_realtime.RtEpochs. In general it is not recommended in a normal processing pipeline as it may result in edge artifacts. Use with caution.

Methods

`fit`(epochs_data, y)	Filter data.
`fit_transform`(X[, y])	Fit to data, then transform it.
`transform`(epochs_data)	Filter data.

fit(epochs_data, y)[source]#

Filter data.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels, n_times): The data.
yarray, shape (n_epochs,): The label for each epoch.

Returns:

selfinstance of FilterEstimator: The modified instance.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params, and returns a transformed version of X.

Parameters:

Xarray, shape (n_samples, n_features): Training set.
yarray, shape (n_samples,): Target values or class labels.
**fit_paramsdict: Additional fitting parameters passed to the fit method..

Returns:

X_newarray, shape (n_samples, n_features_new): Transformed array.

transform(epochs_data)[source]#

Filter data.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels, n_times): The data.

Returns:

Xarray, shape (n_epochs, n_channels, n_times): The data after filtering.