Hello all, I've used the toolbox to analyze my data and I get kind of strange results. What I did is the following: I did a searchlight analysis (radius 10 mm), with the classifier RbfNuSVMC on the whole brain (with a whole brain mask). I used a NFoldCrossvalidation (no detrending or z-scoring). I use two stimuluscategories. The task I used consisted of 38 chunks (38 trials) with in each chunk two stimuluspresentations (one of each category). I have used blockaveraging to reduce features. My results are in the form of 3-d accuracymaps (i.e., for each voxel I have the mean True pos + true neg / total over the crossvalidation). Because I have two stimuluscategories the chance level accuracy would thus be 0.5 What I get as results is that almost all voxels have an accuracy above 0.5 and the highest accuracies I got were around 0.7 (i.e., 70% correctly classified) So this would mean that there is predictive information in all regions of the brain.. Also, when I put all the single subject results into a group analysis I find that all brain regions predict above 50% accuracy. The highest peaks are located at the borders of the brain. My question is: Are these results possibly correct? Is it possible that all brain areas are predictive? Or is there something strange going on? (overfitting maybe?) Best regards, Nynke van der Laan
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