mne.preprocessing.
Xdawn
(n_components=2, signal_cov=None, correct_overlap='auto', reg=None)[source]¶Implementation of the Xdawn Algorithm.
Xdawn [1] [2] 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 nontarget response. This implementation is a generalization to any type of ERP.
Parameters: 


See also
Notes
New in version 0.10.
References
[1]  (1, 2) Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009). xDAWN algorithm to enhance evoked potentials: application to braincomputer interface. Biomedical Engineering, IEEE Transactions on, 56(8), 20352043. 
[2]  (1, 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. 13821386). IEEE. 
Attributes: 


Methods
__hash__ ($self, /) 
Return hash(self). 
apply (inst[, event_id, include, exclude]) 
Remove selected components from the signal. 
fit (epochs[, y]) 
Fit Xdawn from epochs. 
fit_transform (X[, y]) 
Fit to data, then transform it. 
get_params ([deep]) 
Get parameters for this estimator. 
inverse_transform () 
Not implemented, see Xdawn.apply() instead. 
set_params (**params) 
Set the parameters of this estimator. 
transform (inst) 
Apply Xdawn dim reduction. 
__hash__
($self, /)¶Return hash(self).
apply
(inst, event_id=None, include=None, exclude=None)[source]¶Remove selected components from the signal.
Given the unmixing matrix, transform data, zero out components, and inverse transform the data. This procedure will reconstruct the signals from which the dynamics described by the excluded components is subtracted.
Parameters: 


Returns: 

fit
(epochs, y=None)[source]¶Fit Xdawn from epochs.
Parameters: 


Returns: 

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: 


Returns: 

get_params
(deep=True)[source]¶Get parameters for this estimator.
Parameters: 


Returns: 

set_params
(**params)[source]¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
Returns
——
self