mne.minimum_norm.InverseOperator#
- class mne.minimum_norm.InverseOperator[source]#
InverseOperator class to represent info from inverse operator.
- Attributes:
Methods
__contains__
(key, /)True if the dictionary has the specified key, else False.
__getitem__
(key, /)Return self[key].
__iter__
(/)Implement iter(self).
__len__
(/)Return len(self).
clear
()copy
()Return a copy of the InverseOperator.
fromkeys
(iterable[, value])Create a new dictionary with keys from iterable and values set to value.
get
(key[, default])Return the value for key if key is in the dictionary, else default.
items
()keys
()pop
(key[, default])If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem
(/)Remove and return a (key, value) pair as a 2-tuple.
setdefault
(key[, default])Insert key with a value of default if key is not in the dictionary.
update
([E, ]**F)If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values
()- __contains__(key, /)#
True if the dictionary has the specified key, else False.
- __getitem__(key, /)#
Return self[key].
- __iter__(/)#
Implement iter(self).
- __len__(/)#
Return len(self).
- property ch_names#
Name of channels attached to the inverse operator.
- clear() None. Remove all items from D. #
- fromkeys(iterable, value=None, /)#
Create a new dictionary with keys from iterable and values set to value.
- get(key, default=None, /)#
Return the value for key if key is in the dictionary, else default.
- items() a set-like object providing a view on D's items #
- keys() a set-like object providing a view on D's keys #
- pop(key, default=<unrepresentable>, /)#
If the key is not found, return the default if given; otherwise, raise a KeyError.
- popitem(/)#
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.
- setdefault(key, default=None, /)#
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
- update([E, ]**F) None. Update D from dict/iterable E and F. #
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
- values() an object providing a view on D's values #
Examples using mne.minimum_norm.InverseOperator
#
![](../_images/sphx_glr_60_ctf_bst_auditory_thumb.png)
Working with CTF data: the Brainstorm auditory dataset
![](../_images/sphx_glr_80_opm_processing_thumb.png)
Preprocessing optically pumped magnetometer (OPM) MEG data
![](../_images/sphx_glr_30_mne_dspm_loreta_thumb.gif)
Source localization with MNE, dSPM, sLORETA, and eLORETA
![](../_images/sphx_glr_35_dipole_orientations_thumb.png)
The role of dipole orientations in distributed source localization
![](../_images/sphx_glr_70_eeg_mri_coords_thumb.png)
EEG source localization given electrode locations on an MRI
![](../_images/sphx_glr_20_cluster_1samp_spatiotemporal_thumb.png)
Permutation t-test on source data with spatio-temporal clustering
![](../_images/sphx_glr_60_cluster_rmANOVA_spatiotemporal_thumb.png)
Repeated measures ANOVA on source data with spatio-temporal clustering
![](../_images/sphx_glr_compute_source_psd_epochs_thumb.png)
Compute Power Spectral Density of inverse solution from single epochs
![](../_images/sphx_glr_source_label_time_frequency_thumb.png)
Compute power and phase lock in label of the source space
![](../_images/sphx_glr_source_power_spectrum_thumb.png)
Compute source power spectral density (PSD) in a label
![](../_images/sphx_glr_source_space_time_frequency_thumb.png)
Compute induced power in the source space with dSPM
![](../_images/sphx_glr_compute_mne_inverse_epochs_in_label_thumb.png)
Compute MNE-dSPM inverse solution on single epochs
![](../_images/sphx_glr_compute_mne_inverse_volume_thumb.png)
Compute MNE-dSPM inverse solution on evoked data in volume source space
![](../_images/sphx_glr_label_source_activations_thumb.png)
Extracting the time series of activations in a label
![](../_images/sphx_glr_mixed_norm_inverse_thumb.png)
Compute sparse inverse solution with mixed norm: MxNE and irMxNE
![](../_images/sphx_glr_mixed_source_space_inverse_thumb.png)
Compute MNE inverse solution on evoked data with a mixed source space
![](../_images/sphx_glr_mne_cov_power_thumb.png)
Compute source power estimate by projecting the covariance with MNE
![](../_images/sphx_glr_multi_dipole_model_thumb.png)
Computing source timecourses with an XFit-like multi-dipole model
![](../_images/sphx_glr_psf_ctf_vertices_thumb.png)
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
![](../_images/sphx_glr_resolution_metrics_thumb.png)
Compute spatial resolution metrics in source space
![](../_images/sphx_glr_resolution_metrics_eegmeg_thumb.png)
Compute spatial resolution metrics to compare MEG with EEG+MEG