[FieldTrip] Support vector machine

[Martin Rosenfelder]

Dear fieldtrip community,

I am using the DMLT toolbox' svm in order to classify EEG data in two different conditions. More precisely, the SVM is trained on trigger-based trials for a mental motor imagery condition and a resting state condition.
The classifier works fine with accuracy rates between 50-80 %. In order to boost the performance of the classifier I would now like to look into the parameterization of the classifier, e.g. how the classifier is build. This includes decision rules the classifier uses in order to analyze the data I am feeding in.
I was able to detect the SVM behind the ft_crossvalidate function but not how it works or how I can customize it.
Is there a possibility to 'personalize' the SVM in the DMLT Toolbox?

Thank you very much in advance for your help and effort!

Best,
Martin
-- 
M.Sc.-Psych. Martin Rosenfelder
Wissenschaftlicher Mitarbeiter
Klinische und Biologische Psychologie
Universität Ulm
Raum 47.2.259
+49 731-50 26592
martin.rosenfelder at uni-ulm.de