mne.cov.compute_whitener

mne.cov.compute_whitener(noise_cov, info=None, picks=None, rank=None, scalings=None, return_rank=False, pca=False, return_colorer=False, verbose=None)

Compute whitening matrix.

Parameters

noise_covCovariance

The noise covariance.

infodict | None

The measurement info. Can be None if noise_cov has already been prepared with prepare_noise_cov().

picksstr | list | slice | None

Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels (excluding reference MEG channels). Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

rankNone | ‘info’ | ‘full’ | dict

This controls the rank computation that can be read from the measurement info or estimated from the data.

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 None.

New in version 0.18: Support for ‘info’ mode.

scalingsdict | None

The rescaling method to be applied. See documentation of prepare_noise_cov for details.

return_rankbool

If True, return the rank used to compute the whitener.

New in version 0.15.

pcabool | str

Space to project the data into. Options:

True

Whitener will be shape (n_nonzero, n_channels).

'white'

Whitener will be shape (n_channels, n_channels), potentially rank deficient, and have the first n_channels - n_nonzero rows and columns set to zero.

False (default)

Whitener will be shape (n_channels, n_channels), potentially rank deficient, and rotated back to the space of the original data.

New in version 0.18.

return_colorerbool

If True, return the colorer as well.

verbosebool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more). If used, it should be passed as a keyword-argument only.

Returns

Wndarray, shape (n_channels, n_channels) or (n_nonzero, n_channels)

The whitening matrix.

ch_nameslist

The channel names.

rankint

Rank reduction of the whitener. Returned only if return_rank is True.

colorerndarray, shape (n_channels, n_channels) or (n_channels, n_nonzero)

The coloring matrix.

Examples using mne.cov.compute_whitener