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 version 0.9.0.

Examples using `mne.combine_evoked`#

Overview of MEG/EEG analysis with MNE-Python

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Auto-generating Epochs metadata

The Evoked data structure: evoked/averaged data

EEG processing and 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

Sensor Space Data

mne.concatenate_raws

mne.combine_evoked#

Examples using mne.combine_evoked#

Examples using `mne.combine_evoked`#