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 v0.9.0.
Examples using mne.combine_evoked
#
![](../_images/sphx_glr_60_ctf_bst_auditory_thumb.png)
Working with CTF data: the Brainstorm auditory dataset
![](../_images/sphx_glr_70_fnirs_processing_thumb.png)
Preprocessing functional near-infrared spectroscopy (fNIRS) data
![](../_images/sphx_glr_20_dipole_fit_thumb.png)
Source localization with equivalent current dipole (ECD) fit
![](../_images/sphx_glr_20_erp_stats_thumb.png)
Visualising statistical significance thresholds on EEG data
![](../_images/sphx_glr_50_cluster_between_time_freq_thumb.png)
Non-parametric between conditions cluster statistic on single trial power
![](../_images/sphx_glr_limo_data_thumb.png)
Single trial linear regression analysis with the LIMO dataset