Feedback from the final presentation
Feedback from the final presentation¶
visualise things in MRIcroGL, soon implemented in nilearn as
niviewchange my pearson-r values to [-1, 1] (currently [-100,100])
for comparing nilearn and spm beta maps: specify not-matched. E.g.:
between software, within subject, within contrast, within run
between software, between subject, between contrast, between run
feature selection must be either on (A) train set or (B) integrated in cross-validation. Make sure it’s not selecting featuers on the entier dataset –> danger of overfitting!
r2 is a measure for the percentage of variance explained. Not relevant here, thus, remove it.
explain permutation test for significance testing.
apply the classifer on beta maps per trials (there are 10 trials per run). I can use ROIs or entire beta masks. The model will take care of the many variables.
try a searchlight appraoch: train a classifer on different ROIs or voxels and then compare the accuracy. This could lead to a accuracy map with voxel resolution, however, this is computationally expensive.
it’s important to de-mean my data before doing this (e.g. use z-score).
look into stratified cross validation to deal with multiple runs per subject in the classifier. If I use stratification this shouldn’t be a problem.