I analyse evoked responses to continuous predictors on 64-channel EEG data. I have adopted an approach from Nieuwland et al. (2020) where I run mixed-effects regression models over all electrodes and timepoints and control for multiple comparisons.
Raw data and epoch extraction, including single-trial participant and item metadata, is done in MNE Python but I export data to R for sensor-level statistical analysis and plotting.
Is it possible to do something similar to what I am currently doing in source space?
I have tried calculating evoked potentials in MNE using the relevant metadata (e.g. the response to the predictor “word frequency”) following this tutorial :
https://mne.tools/stable/auto_examples/stats/sensor_regression.html
then doing source localisation on the evoked responses using this tutori al:
I can get source-level estimates for individual participants. Am I right to assume that these are estimates of the evoked potentials for the continuous predi ctor?
If yes, can anyone guide me in the right direction for running a group-level analysis in source space? This is of course complicated by there being many more vertices than channels.
Could I run a loop with evoked estimates, noise covariance matrices, forward solutions etc. and then concatenate the stcs? Or would it be better to calculate single-trial source estimates for all participants, channels and time points and then do statistical testing in source space?
I found this discussion relevant but I don’t know how to move forward from here.
https://mne.discourse.group/t/computing-regression-on-sensor-data-then-transforming-to- source-space/669/3
Any help and recommendation is much appreciated!
MNE version: 1.11. 0
OS: mac OS