mne.minimum_norm.resolution_metrics#

mne.minimum_norm.resolution_metrics(resmat, src, function='psf', metric='peak_err', threshold=0.5, verbose=None)[source]#

Compute spatial resolution metrics for linear solvers.

Parameters
resmatarray, shape (n_orient * n_vertices, n_vertices)

The resolution matrix. If not a square matrix and if the number of rows is a multiple of number of columns (e.g. free or loose orientations), then the Euclidean length per source location is computed (e.g. if inverse operator with free orientations was applied to forward solution with fixed orientations).

srcinstance of SourceSpaces

Source space object from forward or inverse operator.

function‘psf’ | ‘ctf’

Whether to compute metrics for columns (point-spread functions, PSFs) or rows (cross-talk functions, CTFs) of the resolution matrix.

metricstr

The resolution metric to compute. Allowed options are:

Localization-based metrics:

  • 'peak_err' Peak localization error (PLE), Euclidean distance between peak and true source location.

  • 'cog_err' Centre-of-gravity localisation error (CoG), Euclidean distance between CoG and true source location.

Spatial-extent-based metrics:

  • 'sd_ext' Spatial deviation (e.g. 12).

  • 'maxrad_ext' Maximum radius to 50% of max amplitude.

Amplitude-based metrics:

  • 'peak_amp' Ratio between absolute maximum amplitudes of peaks per location and maximum peak across locations.

  • 'sum_amp' Ratio between sums of absolute amplitudes.

thresholdfloat

Amplitude fraction threshold for spatial extent metric ‘maxrad_ext’. Defaults to 0.5.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns
resolution_metricinstance of SourceEstimate

The resolution metric.

Notes

For details, see 12.

New in version 0.20.

References

1(1,2)

Molins A, Stufflebeam S. M., Brown E. N., and Hämäläinen M. S. Quantification of the benefit from integrating MEG and EEG data in minimum l2-norm estimation. Neuroimage, 42(3):1069–1077, 2008. doi:10.1016/j.neuroimage.2008.05.064.

2(1,2)

Olaf Hauk, Matti Stenroos, and Matthias Treder. Towards an objective evaluation of EEG/MEG source estimation methods: the linear tool kit. bioRxiv, 2019. doi:10.1101/672956.

Examples using mne.minimum_norm.resolution_metrics#

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

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