Decoding based on single-trial estimate

Dear Martin,

I would like to do trial-wise decoding. Since the example you provided is based on run-wise beta estimates, I have a few questions about the GLM specification and creation in this case:

  1. My task is a 200-trial (5 runs) ER design, with 3 events in each trial: fixation, cue, action. I am interested in the cue phase and would like to classify the reward magnitude (high vs. low). Can I use 200 regressors to represent 200 cues respectively (and another regressor of no interest to model all action phases), and use the generated 200 beta images for the decoding analysis?

  2. I can either group these 200 beta images based on 5 runs (40 trials in each) or treat them separately. Should I do a leave-one-run-out or leave-one-trial-out CV? which do you recommend?

  3. Since the reward magnitude is estimated from a model based on subjects’ actions, the number of high- and low-reward trials may be not equal in each run. Is it necessary to do bootstrap samples to maintain the balance of the training data?

Many thanks!