mne.preprocessing.Xdawn#

class mne.preprocessing.Xdawn(n_components=2, signal_cov=None, correct_overlap='auto', reg=None, *, rank='full')[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 non-target response. This implementation is a generalization to any type of ERP.

Parameters:

n_componentsint

The number of components to decompose the signals.

signal_covNone | 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’ | bool

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.

regfloat | str | None

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 as method to mne.compute_covariance().

rankNone | ‘info’ | ‘full’ | dict

This controls the rank computation that can be read from the measurement info or estimated from the data. When a noise covariance is used for whitening, this should reflect the rank of that covariance, otherwise amplification of noise components can occur in whitening (e.g., often during source localization).

None

The rank will be estimated from the data after proper scaling of different channel types.

'info'

The rank is inferred from info. If data have been processed with Maxwell filtering, the Maxwell filtering header is used. Otherwise, the channel counts themselves are used. In both cases, the number of projectors is subtracted from the (effective) number of channels in the data. For example, if Maxwell filtering reduces the rank to 68, with two projectors the returned value will be 66.

'full'

The rank is assumed to be full, i.e. equal to the number of good channels. If a Covariance is passed, this can make sense if it has been (possibly improperly) regularized without taking into account the true data rank.

dict

Calculate the rank only for a subset of channel types, and explicitly specify the rank for the remaining channel types. This can be extremely useful if you already know the rank of (part of) your data, for instance in case you have calculated it earlier.

This parameter must be a dictionary whose keys correspond to channel types in the data (e.g. 'meg', 'mag', 'grad', 'eeg'), and whose values are integers representing the respective ranks. For example, {'mag': 90, 'eeg': 45} will assume a rank of 90 and 45 for magnetometer data and EEG data, respectively.

The ranks for all channel types present in the data, but not specified in the dictionary will be estimated empirically. That is, if you passed a dataset containing magnetometer, gradiometer, and EEG data together with the dictionary from the previous example, only the gradiometer rank would be determined, while the specified magnetometer and EEG ranks would be taken for granted.

The default is 'full'. If not 'full', the covariances are restricted to their rank-dimensional principal subspace before computing the spatial filters, which are then projected back out to the full sensor space. This is useful for rank-deficient data, e.g., data with SSP projectors applied or that has been Maxwell filtered.

New in v1.13.

Attributes:

filters_dict of ndarray: 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 (n_channels rows, or rank rows if rank is not 'full').
patterns_dict of ndarray: If fit, the Xdawn patterns used to restore the signals for each event type, else empty.
evokeds_dict of Evoked: If fit, the evoked response for each event type.
event_id_dict: The event id.
correct_overlap_bool: Whether overlap correction was applied.

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.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`()	Not implemented, see Xdawn.apply() instead.
`save`(fname, *[, overwrite, verbose])	Save the object to disk (in HDF5 format).
`set_fit_request`(*[, epochs])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`set_transform_request`(*[, inst])	Configure whether metadata should be requested to be passed to the `transform` method.
`transform`(inst)	Apply Xdawn dim reduction.

Examples using `mne.preprocessing.Xdawn`#

XDAWN Denoising

Decoding (MVPA)

mne.preprocessing.Xdawn#

Examples using mne.preprocessing.Xdawn#

Examples using `mne.preprocessing.Xdawn`#