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 [R49] [R50] 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)

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 (default None)

if not None, allow regularization for covariance estimation if float, shrinkage covariance is used (0 <= shrinkage <= 1). if str, optimal shrinkage using Ledoit-Wolf Shrinkage (‘ledoit_wolf’) or Oracle Approximating Shrinkage (‘oas’).

See also

mne.decoding.CSP

Notes

New in version 0.10.

References

[R49](1, 2) 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.
[R50](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. 1382-1386). IEEE.

Attributes

filters_ (dict of ndarray) If fit, the Xdawn components used to decompose the data for each event type, else empty.
patterns_ (dict of ndarray) If fit, the Xdawn patterns used to restore the signals for each event type, else empty.
evokeds_ (dict of evoked instance) If fit, the evoked response for each event type.
event_id_ (dict of event id) The event id.
correct_overlap_: bool Whether overlap correction was applied.

Methods

__hash__() <==> hash(x)
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(epochs) Apply Xdawn dim reduction.
__hash__() <==> hash(x)
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:

inst : instance of Raw | Epochs | Evoked

The data to be processed.

event_id : dict | list of str | None (default None)

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.

include : array_like of int | None (default None)

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.

exclude : array_like of int | None (default None)

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.
Returns:

out : dict of instance

A dict of instance (from the same type as inst input) for each event type in event_id.
fit(epochs, y=None)[source]

Fit Xdawn from epochs.

Parameters:

epochs : Epochs object

An instance of Epoch on which Xdawn filters will be fitted.

y : ndarray | None (default None)

If None, used epochs.events[:, 2].
Returns:

self : Xdawn instance

The Xdawn instance.
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:

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.
Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:

deep : boolean, optional

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

params : mapping of string to any

Parameter names mapped to their values.
inverse_transform()[source]

Not implemented, see Xdawn.apply() instead.

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

transform(epochs)[source]

Apply Xdawn dim reduction.

Parameters:

epochs : Epochs | ndarray, shape (n_epochs, n_channels, n_times)

Data on which Xdawn filters will be applied.
Returns:

X : ndarray, shape (n_epochs, n_components * n_event_types, n_times)

Spatially filtered signals.