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=1, method=’fft’, iir_params=None, 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:

info : instance of Info Measurement info. l_freq : float | None Low cut-off frequency in Hz. If None the data are only low-passed. h_freq : float | None High cut-off frequency in Hz. If None the data are only high-passed. picks : array-like of int | None Indices of channels to filter. If None only the data (MEG/EEG) channels will be filtered. filter_length : str (Default: ‘10s’) | int | None Length of the filter to use. If None or “len(x) < filter_length”, the filter length used is len(x). Otherwise, if int, overlap-add filtering with a filter of the specified length in samples) is used (faster for long signals). If str, a human-readable time in units of “s” or “ms” (e.g., “10s” or “5500ms”) will be converted to the shortest power-of-two length at least that duration. l_trans_bandwidth : float Width of the transition band at the low cut-off frequency in Hz. h_trans_bandwidth : float Width of the transition band at the high cut-off frequency in Hz. 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=’fft’. method : str ‘fft’ will use overlap-add FIR filtering, ‘iir’ will use IIR forward-backward filtering (via filtfilt). 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. verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more). Defaults to self.verbose.

See also

TemporalFilter

Methods

__hash__() <==> hash(x)
fit(epochs_data, y)	Filter data.
fit_transform(X[, y])	Fit to data, then transform it.
transform(epochs_data[, y])	Filter data.

__hash__() <==> hash(x)¶

fit(epochs_data, y)[source]¶

Filter data.

Parameters:	epochs_data : array, shape (n_epochs, n_channels, n_times) The data. y : array, shape (n_epochs,) The label for each epoch.
Returns:	self : instance of FilterEstimator Returns 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:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

transform(epochs_data, y=None)[source]¶

Filter data.

Parameters:	epochs_data : array, shape (n_epochs, n_channels, n_times) The data. y : None | array, shape (n_epochs,) The label for each epoch. If None not used. Defaults to None.
Returns:	X : array, shape (n_epochs, n_channels, n_times) The data after filtering