[FieldTrip] Question regarding multiple comparison correction for mixed linear models on individual electrodes and time points

[二姬]

Hello everyone,

I am Chengyuan Wu, and I appreciate any guidance you can provide on this
matter. Recently, I encountered an issue regarding the cluster-based
permutation test. I am utilizing a mixed linear model to fit EEG data at
each electrode and each time point using a trial-by-trial variable
(probability of expected reward) inferred from a Bayesian model. The
subject number is set as a random intercept, and data from all subjects are
fed into each mixed linear model simultaneously. My EEG data dimension is
102 electrodes x 500 time points, leading me to fit 51,000 mixed linear
models and test the significance of the fixed slope (t-test). Previously,
I've been applying FDR correction across the 500 time points for each
electrode, but I believe a cluster-based permutation test might be a more
robust approach. I've gone through the tutorials on FieldTrip, but they
seem to primarily cater to ERP data driven by categorical variables.

I have two main questions:

1.Does FieldTrip offer a standard method for multiple comparison correction
using cluster-based permutation tests specifically for mixed linear models?

   1.
   2. 2.If there isn't a standard method, has anyone come across literature
   where a similar approach of cluster-based permutation tests for mixed
   linear models has been employed? If so, could you please share those
   references?

Thank you for your assistance!

Best regards, Chengyuan Wu School of Psychology, South China Normal
University
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