TDT: Decoding having only one run

Hello everyone!

I am very new to MVPA and I have a question.

The decoding examples I have seen so far (including the template script from TDT) refer to data collected in several runs, where you use some runs for training and some for testing.

I have one long run only. In this run, my conditions are repeated several times (let’s say n). Couldn’t I use the (n-1) repetitions to train my classifier and test it on the nth… etc? Or then the test and training data are not independent because they belong to the same run?
I have tried such SPM.mat but I get an error that the chunk is only 1, I suppose because of the one run:

“Empty decoding steps found in design in step(s) 1. Maybe you have only one chunk and want to do cross-validation (which doesn’t make sense).”

Should I create the SPM.mat so that each repetition is one run? It does not feel correct to do so though since I only have one run. Does anybody know?

I would appreciate any advice!

Thank you!

Hi Konstantina,

Since there was no tag, I didn’t see this post earlier, apologies! If you still need a reply, please let me know! The short version: set up the design manually and make sure you get several beta estimates for your different conditions. Check out our templates for that! But make sure that the data in different folds is as independent as possible.


Hi Martin,

Can you please elaborate on how I should set the design manually? Do you mean I should run a GLM using TDT myself?

I have the same issue as the original author here. I have one run, and I have 2 regressors, which are divided to 4 chunks. I have enough repetitions (20 or so per chunk), so that should be fine. I believe I should be using the unbalanced data script. I tried one of the methods explained in that file but the original error remains.

P.S. Plus, a small portion of my participants have 3 runs. So I am curious if it’s possible to perform searchlight on all my participants at the same time (both 3 run and single run participants)? Or shall I do that seperately?