mne.combine_evoked#
- mne.combine_evoked(all_evoked, weights)[source]#
Merge evoked data by weighted addition or subtraction.
Each
Evoked
inall_evoked
should have the same channels and the same time instants. Subtraction can be performed by passingweights=[1, -1]
.Warning
Other than cases like simple subtraction mentioned above (where all weights are -1 or 1), if you provide numeric weights instead of using
'equal'
or'nave'
, the resultingEvoked
object’s.nave
attribute (which is used to scale noise covariance when applying the inverse operator) may not be suitable for inverse imaging.- Parameters:
- all_evoked
list
ofEvoked
The evoked datasets.
- weights
list
offloat
| ‘equal’ | ‘nave’ The weights to apply to the data of each evoked instance, or a string describing the weighting strategy to apply:
'nave'
computes sum-to-one weights proportional to each object’snave
attribute;'equal'
weights eachEvoked
by1 / len(all_evoked)
.
- all_evoked
- Returns:
- evoked
Evoked
The new evoked data.
- evoked
Notes
New in version 0.9.0.
Examples using mne.combine_evoked
#
Overview of MEG/EEG analysis with MNE-Python
Working with CTF data: the Brainstorm auditory dataset
Preprocessing functional near-infrared spectroscopy (fNIRS) data
Regression-based baseline correction
Auto-generating Epochs metadata
The Evoked data structure: evoked/averaged data
EEG analysis - Event-Related Potentials (ERPs)
Source localization with equivalent current dipole (ECD) fit
Visualising statistical significance thresholds on EEG data
Non-parametric between conditions cluster statistic on single trial power
Regression on continuous data (rER[P/F])
Single trial linear regression analysis with the LIMO dataset
From raw data to dSPM on SPM Faces dataset