class pyriemann.spatialfilters.Xdawn(nfilter=4, classes=None, estimator='scm', baseline_cov=None)

Xdawn algorithm.

Xdawn [1] is a spatial filtering method designed to improve the signal to signal + noise ratio (SSNR) of the ERP responses. Xdawn was originaly designed for P300 evoked potential by enhancing the target response with respect to the non-target response [2]. This implementation is a generalization to any type of ERP.

nfilterint, default=4

The number of components to decompose M/EEG signals.

classeslist of int | None, default=None

List of classes to take into account for Xdawn. If None, all classes will be accounted.

estimatorstring, default=’scm’

Covariance matrix estimator, see pyriemann.utils.covariance.covariances().

baseline_covNone | array, shape(n_channels, n_channels), default=None

Covariance matrix to which the average signals are compared. If None, the baseline covariance is computed across all trials and time samples.

See also




xDAWN algorithm to enhance evoked potentials: application to brain-computer interface B. Rivet, A. Souloumiac, V. Attina, and G. Gibert. IEEE Transactions on Biomedical Engineering, 2009, 56 (8), pp.2035-43.


Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI B. Rivet, H. Cecotti, A. Souloumiac, E. Maby, J. Mattout. EUSIPCO 2011 19th European Signal Processing Conference, Aug 2011, Barcelone, Spain. pp.1382-1386.

classes_ndarray, shape (n_classes,)

Labels for each class.

filters_ndarray, shape (n_classes x min(n_channels, n_filters), n_channels)

If fit, the Xdawn components used to decompose the data for each event type, concatenated.

patterns_ndarray, shape (n_classes x min(n_channels, n_filters), n_channels)

If fit, the Xdawn patterns used to restore M/EEG signals for each event type, concatenated.

evokeds_ndarray, shape (n_classes x min(n_channels, n_filters), n_times)

If fit, the evoked response for each event type, concatenated.

__init__(nfilter=4, classes=None, estimator='scm', baseline_cov=None)


fit(X, y)

Train Xdawn spatial filters.

Xndarray, shape (n_trials, n_channels, n_times)

Set of trials.

yndarray, shape (n_trials,)

Labels for each trial.

selfXdawn instance

The Xdawn instance.

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.

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.


A MetadataRequest encapsulating routing information.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.

set_output(*, transform=None)

Set output container.

See for an example on how to use the API.

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • None: Transform configuration is unchanged

selfestimator instance

Estimator instance.


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.


Estimator parameters.

selfestimator instance

Estimator instance.


Apply spatial filters.

Xndarray, shape (n_trials, n_channels, n_times)

Set of trials.

Xfndarray, shape (n_trials, n_classes x min(n_channels, n_filters), n_times)

Set of spatialy filtered trials.