mne.preprocessing.Xdawn¶

class
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
 n_components
int
, (default 2) The number of components to decompose the signals.
 signal_cov
None
Covariance
ndarray
, shape (n_channels, n_channels) (default None). The signal covariance used for whitening of the data. if None, the covariance is estimated from the epochs signal.
 correct_overlap‘auto’ or bool (default ‘auto’)
Compute the independent evoked responses per condition, while correcting for event overlaps if any. If ‘auto’, then overlapp_correction = True if the events do overlap.
 reg
float
str
None
(defaultNone
) If not None (same as
'empirical'
, default), allow regularization for covariance estimation. If float, shrinkage is used (0 <= shrinkage <= 1). For str options,reg
will be passed asmethod
tomne.compute_covariance()
.
 n_components
See also
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 braincomputer interface. Biomedical Engineering, IEEE Transactions on, 56(8), 20352043.
 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
 filters_
dict
ofndarray
If fit, the Xdawn components used to decompose the data for each event type, else empty. For each event type, the filters are in the rows of the corresponding array.
 patterns_
dict
ofndarray
If fit, the Xdawn patterns used to restore the signals for each event type, else empty.
 evokeds_
dict
ofEvoked
If fit, the evoked response for each event type.
 event_id_
dict
The event id.
 correct_overlap_bool
Whether overlap correction was applied.
 filters_
Methods
__hash__
(self, /)Return hash(self).
apply
(self, inst[, event_id, include, exclude])Remove selected components from the signal.
fit
(self, epochs[, y])Fit Xdawn from epochs.
fit_transform
(self, X[, y])Fit to data, then transform it.
get_params
(self[, deep])Get parameters for this estimator.
inverse_transform
(self)Not implemented, see Xdawn.apply() instead.
set_params
(self, **params)Set the parameters of this estimator.
transform
(self, inst)Apply Xdawn dim reduction.

__hash__
(self, /)¶ Return hash(self).

apply
(self, 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
 instinstance of
Raw
Epochs
Evoked
The data to be processed.
 event_id
dict
list
ofstr
None
(defaultNone
) The kind of event to apply. if None, a dict of inst will be return one for each type of event xdawn has been fitted.
 includearray_like of
int
None
(defaultNone
) The indices referring to columns in the ummixing matrix. The components to be kept. If None, the first n_components (as defined in the Xdawn constructor) will be kept.
 excludearray_like of
int
None
(defaultNone
) The indices referring to columns in the ummixing matrix. The components to be zeroed out. If None, all the components except the first n_components will be exclude.
 instinstance of
 Returns
 out
dict
A dict of instance (from the same type as inst input) for each event type in event_id.
 out
Examples using
apply
:

fit
(self, epochs, y=None)[source]¶ Fit Xdawn from epochs.
 Parameters
 Returns
 selfinstance of
Xdawn
The Xdawn instance.
 selfinstance of
Examples using
fit
:

fit_transform
(self, 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.

set_params
(self, **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