Merge evoked data by weighted addition or subtraction.
Each Evoked
in all_evoked
should have the same channels and the
same time instants. Subtraction can be performed by passing
weights=[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 resulting Evoked
object’s
.nave
attribute (which is used to scale noise covariance when
applying the inverse operator) may not be suitable for inverse imaging.
list
of Evoked
The evoked datasets.
list
of float
| ‘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’s nave
attribute;
'equal'
weights each Evoked
by 1 / len(all_evoked)
.
Evoked
The new evoked data.
Notes
New in version 0.9.0.
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