Hi, I m really new to machine learning and have just collected some fMRI data for analysis with PyMVPA
The design is simple, basically I have 2 tasks, S and I and each task has 2 conditions: a and b Each task occurs on a separate fMRI run and the conditions a & b are blocked such as 'ababbaabbaabbaba' (each block is 4 trials each Data has been preprocessed in FSL (as part of univariate-based analyses), including a 5 mm smoothing. I have derived parameter estimates for each task condition a & b....so have 8 betas per subject per condition. Basically I would like to train a SVM classifier to discriminate conditions a & b in task S and then test it on the independent dataset from the different task I. For this I thought to normalise to MNI and concatenate all the arameter estimates for a & b for task S across all subjects and in principle use whole-brain classification, with the intention of trying searchligh analyses later on... Does this make sense? or would it be better to do it differently? Any advise or pointers would be much appreciated! cheers, david -- http://www1.imperial.ac.uk/medicine/people/d.soto/
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