mne.combine_evoked#

mne.combine_evoked(all_evoked, weights)[source]#

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.

Parameters:

all_evokedlist of Evoked: The evoked datasets.
weightslist 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).

Returns:

evokedEvoked: The new evoked data.

Notes

New in v0.9.0.

Examples using `mne.combine_evoked`#

Single trial linear regression analysis with the LIMO dataset

From raw data to dSPM on SPM Faces dataset

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Regression-based baseline correction

Auto-generating Epochs metadata

The Evoked data structure: evoked/averaged data

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Overview of MEG/EEG analysis with MNE-Python

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Working with CTF data: the Brainstorm auditory dataset

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Visualising statistical significance thresholds on EEG data

Non-parametric between conditions cluster statistic on single trial power

Using the event system to link figures

mne.combine_evoked#

Examples using mne.combine_evoked#

Examples using `mne.combine_evoked`#