mne.minimum_norm.read_inverse_operator

mne.minimum_norm.read_inverse_operator(fname, verbose=None)

Read the inverse operator decomposition from a FIF file.

Parameters

fnamestr

The name of the FIF file, which ends with -inv.fif or -inv.fif.gz.

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

invinstance of InverseOperator

The inverse operator.

See also

write_inverse_operator, make_inverse_operator

Examples using mne.minimum_norm.read_inverse_operator

Overview of MEG/EEG analysis with MNE-Python

Visualize source time courses (stcs)

Permutation t-test on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Corrupt known signal with point spread

Plot the MNE brain and helmet

Compute Power Spectral Density of inverse solution from single epochs

Compute power and phase lock in label of the source space

Compute source power spectral density (PSD) in a label

Compute induced power in the source space with dSPM

Decoding source space data

Compute coherence in source space using a MNE inverse solution

Compute full spectrum source space connectivity between labels

Compute source space connectivity and visualize it using a circular graph

Compute Phase Slope Index (PSI) in source space for a visual stimulus

Compute MNE-dSPM inverse solution on single epochs

Compute sLORETA inverse solution on raw data

Compute MNE-dSPM inverse solution on evoked data in volume source space

Generate a functional label from source estimates

Extracting the time series of activations in a label

Morph volumetric source estimate

Visualize source leakage among labels using a circular graph

Reading an inverse operator

Estimate data SNR using an inverse

Plotting the full vector-valued MNE solution

mne.minimum_norm.prepare_inverse_operator mne.minimum_norm.source_band_induced_power