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FiffCov

Namespace: FIFFLIB  ·  Library: FIFF Library

Python equivalent

mne.Covariance in MNE-Python.

#include <fiff/fiff_cov.h>

class FIFFLIB::FiffCov

FIFF noise / data covariance: matrix, channel names, kind, applied projectors, bads, dof and optional whitening eigendecomposition.

Round-trips with *-cov.fif files and with mne.Covariance in MNE-Python. Carries both the raw matrix and the metadata needed to regularize, project away SSP subspaces, and pre-whiten downstream forward / inverse computations.

Inheritance​

Public Methods​

FiffCov()​

Constructs the covariance data matrix.

FiffCov(p_IODevice)​

Constructs a covariance data matrix, by reading from a IO device.

Parameters:

  • p_IODevice : QIODevice & IO device to read from the evoked data set.

FiffCov(p_FiffCov)​

Copy constructor.

Parameters:

  • p_FiffCov : const FiffCov & Covariance data matrix which should be copied.

~FiffCov()​

Destroys the covariance data matrix.

clear()​

Initializes the covariance data matrix.

isEmpty()​

True if FIFF covariance is empty.

Returns:

  • bool — true if FIFF covariance is empty.

pick_channels(p_include, p_exclude)​

python pick_channels_cov

Pick channels from covariance matrix

Parameters:

  • p_include : const QStringList & List of channels to include (if empty, include all available). (optional).

  • p_exclude : const QStringList & Channels to exclude (if empty, do not exclude any). (optional).

Returns:

  • FiffCov — Covariance solution restricted to selected channels.

prepare_noise_cov(p_info, p_chNames)​

Prepare noise covariance matrix.

Before creating inverse operator.

Parameters:

  • p_info : const FiffInfo & measurement info.

  • p_chNames : const QStringList & Channels which should be taken into account.

Returns:

  • FiffCov — the prepared noise covariance matrix.

regularize(p_info, p_fMag, p_fGrad, p_fEeg, p_bProj, p_exclude)​

Regularize noise covariance matrix.

This method works by adding a constant to the diagonal for each channel type separatly. Special care is taken to keep the rank of the data constant.

Parameters:

  • p_info : const FiffInfo & The measurement info (used to get channel types and bad channels).

  • p_fMag : double Regularization factor for MEG magnetometers.

  • p_fGrad : double Regularization factor for MEG gradiometers.

  • p_fEeg : double Regularization factor for EEG.

  • p_bProj : bool Apply or not projections to keep rank of data.

  • p_exclude : QStringList List of channels to mark as bad. If None, bads channels are extracted from both info['bads'] and cov['bads'].

Returns:

  • FiffCov — the regularized covariance matrix.

save(fileName)​

Save this covariance matrix to a FIFF file.

Parameters:

  • fileName : const QString & Output file path.

Returns:

  • bool — true on success.

operator=(rhs)​

Assignment Operator.

Parameters:

Returns:

  • FiffCov & — the copied covariance matrix.

Static Methods​

compute_from_epochs(raw, events, eventCodes, tmin, tmax, bmin, bmax, doBaseline, removeMean, ignoreMask, delay)​

Compute a noise covariance matrix from raw data based on event-locked epochs.

Ported from compute_cov.c (MNE-C).

Parameters:

  • raw : const FiffRawData & The raw data.

  • events : const Eigen::MatrixXi & Event matrix (nEvents x 3): [sample, before, after].

  • eventCodes : const QList< int > & Which event codes to include.

  • tmin : float Start of time window relative to event (seconds).

  • tmax : float End of time window relative to event (seconds).

  • bmin : float Baseline start (seconds, relative to event). Only used if doBaseline is true.

  • bmax : float Baseline end (seconds, relative to event). Only used if doBaseline is true.

  • doBaseline : bool Whether to apply baseline correction before covariance computation.

  • removeMean : bool Whether to remove sample mean from the covariance estimate.

  • ignoreMask : unsigned int Bit mask ANDed away from event codes before matching (default: 0 = no masking).

  • delay : float Delay in seconds applied to the event sample before extracting the epoch (default: 0).

Returns:

  • FiffCov — The computed noise covariance matrix, or empty FiffCov on failure.

computeGrandAverage(covs)​

Compute a weighted grand-average covariance from multiple covariance matrices, weighting each by its degrees of freedom (nfree).

Parameters:

  • covs : const QList< FiffCov > & List of covariance matrices to combine.

Returns:

  • FiffCov — The grand-average covariance matrix, or empty FiffCov if covs is empty.

Authors of this file​