mne.decoding.TemporalFilter

class mne.decoding.TemporalFilter(l_freq=None, h_freq=None, sfreq=1.0, filter_length='auto', l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1, method='fir', iir_params=None, fir_window='hamming', fir_design='firwin', verbose=None)

Estimator to filter data array along the last dimension.

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

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

See mne.filter.filter_data().

Parameters

l_freqfloat | None

Low cut-off frequency in Hz. If None the data are only low-passed.

h_freqfloat | None

High cut-off frequency in Hz. If None the data are only high-passed.

sfreqfloat, default 1.0

Sampling frequency in Hz.

filter_lengthstr | int, default ‘auto’

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 (7 times the reciprocal of the shortest transition band).

• 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".

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 in 0.14) 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, default 1

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

methodstr, default ‘fir’

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

iir_paramsdict | None, default 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_windowstr, default ‘hamming’

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

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, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

See also

FilterEstimator

Vectorizer

mne.filter.filter_data

Methods

__hash__(/)	Return hash(self).
fit(X[, y])	Do nothing (for scikit-learn compatibility purposes).
fit_transform(X[, y])	Fit to data, then transform it.
transform(X)	Filter data along the last dimension.

fit(X, y=None)

Do nothing (for scikit-learn compatibility purposes).

Parameters

Xarray, shape (n_epochs, n_channels, n_times) or or shape (n_channels, n_times)

The data to be filtered over the last dimension. The channels dimension can be zero when passing a 2D array.

yNone

Not used, for scikit-learn compatibility issues.

Returns

selfinstance of TemporalFilter

The modified instance.

fit_transform(X, y=None, **fit_params)

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.

**fit_paramsdict

Additional fitting parameters passed to self.fit.

Returns

X_newarray, shape (n_samples, n_features_new)

Transformed array.

transform(X)

Filter data along the last dimension.

Parameters

Xarray, shape (n_epochs, n_channels, n_times) or shape (n_channels, n_times)

The data to be filtered over the last dimension. The channels dimension can be zero when passing a 2D array.

Returns

Xarray

The data after filtering.

mne.decoding.Scaler mne.decoding.TimeFrequency