mne.preprocessing.
Xdawn
(n_components=2, signal_cov=None, correct_overlap='auto', reg=None)¶Implementation of the Xdawn Algorithm.
Xdawn is a spatial filtering method designed to improve the signal to signal + noise ratio (SSNR) of the ERP responses. Xdawn was originally designed for P300 evoked potential by enhancing the target response with respect to the non-target response. This implementation is a generalization to any type of ERP.
Parameters: | n_components : int (default 2)
signal_cov : None | Covariance | ndarray, shape (n_channels, n_channels)
correct_overlap : ‘auto’ or bool (default ‘auto’)
reg : float | str | None (default None)
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Attributes: | filters_ : dict of ndarray
patterns_ : dict of ndarray
evokeds_ : dict of evoked instance
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See also
ICA
, CSP
Notes
New in version 0.10.
References
[1] Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009). xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. Biomedical Engineering, IEEE Transactions on, 56(8), 2035-2043.
[2] Rivet, B., Cecotti, H., Souloumiac, A., Maby, E., & Mattout, J. (2011, August). Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI. In Signal Processing Conference, 2011 19th European (pp. 1382-1386). IEEE.
Methods
apply (inst[, event_id, include, exclude]) |
Remove selected components from the signal. |
fit (epochs[, y]) |
Fit Xdawn from epochs. |
fit_transform (X[, y]) |
Fit to data, then transform it |
transform (epochs) |
Apply Xdawn dim reduction. |
__init__
(n_components=2, signal_cov=None, correct_overlap='auto', reg=None)¶init xdawn.
apply
(inst, event_id=None, include=None, exclude=None)¶Remove selected components from the signal.
Given the unmixing matrix, transform data, zero out components, and inverse transform the data. This procedure will reconstruct M/EEG signals from which the dynamics described by the excluded components is subtracted.
Parameters: | inst : instance of Raw | Epochs | Evoked
event_id : dict | list of str | None (default None)
include : array_like of int | None (default None)
exclude : array_like of int | None (default None)
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Returns: | out : dict of instance
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fit
(epochs, y=None)¶Fit Xdawn from epochs.
Parameters: | epochs : Epochs object
y : ndarray | None (default None)
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Returns: | self : Xdawn instance
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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]
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Returns: | X_new : numpy array of shape [n_samples, n_features_new]
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transform
(epochs)¶Apply Xdawn dim reduction.
Parameters: | epochs : Epochs | ndarray, shape (n_epochs, n_channels, n_times)
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Returns: | X : ndarray, shape (n_epochs, n_components * event_types, n_times)
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