mne.minimum_norm.InverseOperator#

class mne.minimum_norm.InverseOperator[source]#

InverseOperator class to represent info from inverse operator.

Methods

__contains__(key, /)

True if the dictionary has the specified key, else False.

__getitem__

x.__getitem__(y) <==> x[y]

__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(k[,d])

If key is not found, d is returned if given, otherwise KeyError is raised

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__()#

x.__getitem__(y) <==> x[y]

__iter__(/)#

Implement iter(self).

__len__(/)#

Return len(self).

clear() None.  Remove all items from D.#
copy()[source]#

Return a copy of the InverseOperator.

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(k[, d]) v, remove specified key and return the corresponding value.#

If key is not found, d is returned if given, otherwise KeyError is raised

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#

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python

Overview of MEG/EEG analysis with MNE-Python
Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset
Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA
The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization
Computing various MNE solutions

Computing various MNE solutions

Computing various MNE solutions
Visualize source time courses (stcs)

Visualize source time courses (stcs)

Visualize source time courses (stcs)
EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI
Permutation t-test on source data with spatio-temporal clustering

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

Permutation t-test on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering

Repeated measures ANOVA on source data with spatio-temporal clustering
Corrupt known signal with point spread

Corrupt known signal with point spread

Corrupt known signal with point spread
DICS for power mapping

DICS for power mapping

DICS for power mapping
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Plot the MNE brain and helmet

Plot the MNE brain and helmet

Plot the MNE brain and helmet
Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs
Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space
Compute source power spectral density (PSD) in a label

Compute source power spectral density (PSD) in a label

Compute source power spectral density (PSD) in a label
Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM
Decoding source space data

Decoding source space data

Decoding source space data
Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs
Compute sLORETA inverse solution on raw data

Compute sLORETA inverse solution on raw data

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

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

Compute MNE-dSPM inverse solution on evoked data in volume source space
Generate a functional label from source estimates

Generate a functional label from source estimates

Generate a functional label from source estimates
Extracting the time series of activations in a label

Extracting the time series of activations in a label

Extracting the time series of activations in a label
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space

Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE
Morph volumetric source estimate

Morph volumetric source estimate

Morph volumetric source estimate
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Reading an inverse operator

Reading an inverse operator

Reading an inverse operator
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
Estimate data SNR using an inverse

Estimate data SNR using an inverse

Estimate data SNR using an inverse
Computing source space SNR

Computing source space SNR

Computing source space SNR
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution
Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data