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)[source]¶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
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See also
Methods
__hash__ ($self, /) |
Return hash(self). |
fit (X[, y]) |
Do nothing (for scikit-learn compatibility purposes). |
fit_transform (X[, y]) |
Fit to data, then transform it. |
transform (X) |
Filter data along the last dimension. |
__hash__
($self, /)¶Return hash(self).
fit
(X, y=None)[source]¶Do nothing (for scikit-learn compatibility purposes).
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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.
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transform
(X)[source]¶Filter data along the last dimension.
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