mne.decoding.
FilterEstimator
(info, l_freq, h_freq, picks=None, filter_length='10s', l_trans_bandwidth=0.5, h_trans_bandwidth=0.5, n_jobs=1, method='fft', iir_params=None, verbose=None)¶Estimator to filter RtEpochs
Applies a zerophase lowpass, highpass, bandpass, or bandstop 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: bandpass filter
 l_freq > h_freq: bandstop filter
 l_freq is not None, h_freq is None: lowpass filter
 l_freq is None, h_freq is not None: highpass 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
l_freq : float  None
h_freq : float  None
picks : arraylike of int  None
filter_length : str (Default: ‘10s’)  int  None
l_trans_bandwidth : float
h_trans_bandwidth : float
n_jobs : int  str
method : str
iir_params : dict  None
verbose : bool, str, int, or None


Methods
fit (epochs_data, y) 
Filters data 
fit_transform (X[, y]) 
Fit to data, then transform it 
transform (epochs_data[, y]) 
Filters data 
__init__
(info, l_freq, h_freq, picks=None, filter_length='10s', l_trans_bandwidth=0.5, h_trans_bandwidth=0.5, n_jobs=1, method='fft', iir_params=None, verbose=None)¶fit
(epochs_data, y)¶Filters data
Parameters:  epochs_data : array, shape (n_epochs, n_channels, n_times)
y : array, shape (n_epochs,)


Returns:  self : instance of FilterEstimator

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:  X : numpy array of shape [n_samples, n_features]
y : numpy array of shape [n_samples]


Returns:  X_new : numpy array of shape [n_samples, n_features_new]

transform
(epochs_data, y=None)¶Filters data
Parameters:  epochs_data : array, shape (n_epochs, n_channels, n_times)
y : None  array, shape (n_epochs,)


Returns:  X : array, shape (n_epochs, n_channels, n_times)
