In my study, I have two conditions (‘real’ and ‘control’) and I am modelling my trials separately as I have a low number of runs. I would like to run a cross-classification, where I train the classifier on the trials from the first run and would like to evaluate the performance of this classifier on the data from run2 and run3. I have a different number of trials in run1 than in run2 & 3 and when I try to run make_design_xclass_cv.m I get one of the follwing two errors:
- “Error using make_design_xclass_cv (line 229)
Wrong number of labels in cfg.files.xclass. Cross classification needs exactly one training and one test set.”
In this case, I find that cfg.files.xclass contains “1” in every cell for some reason.
In this case, the dimensions of cfg.files.descr are 1x84, while name, chunk and label are all 84x1. Can this be part of the problem? In this one, I have entries which look fine to me, like Sn(1) real_1*bf(1) and so on.
If I create the cfg.files.xclass matrix manually based on the info in cfg.files.descr , I get the following error:
- “Error using decoding_basic_checks (line 235)
Number of used labels varies across decoding steps which prevents comparing results across steps. If multiple sets are used, run them separately.”
I am a bit confused about the terminology of the error message here I think. I have two labels, real and control, am I correct? Is the error referring to the issue that I have different number of trials in the train and test dataset? How can I overcome this? I was also thinking about setting the number of steps manually, but I am not sure whether this is a good idea and If so, how can I set this.
Thanks a lot,