[FieldTrip] multiclass svm

Aykut Eken ekenaykut at gmail.com
Fri Nov 10 12:40:05 CET 2017

Dear Marcel,

Instead of using ft_statistics_crossvalidate, you can extract the features and use as input to fitcsvm in MATLAB that provides answers to your questions. After running the model, you can use fitSVMPosterior or fitPosterior to obtain classification probabilities.


Aykut Eken, PhD

Düzce University
Faculty of Engineering
Biomedical Engineering Department

Düzce University, Konuralp Campus,
81620, Konuralp, Düzce, Turkey

Tel: +905366777364
Office Tel: +90380542 11 00 /4546
Mail address1: aykuteken at duzce.edu.tr
Mail address2: ekenaykut at gmail.com

> On 10 Nov 2017, at 14:04, Bastiaansen, Marcel <Bastiaansen4.M at nhtv.nl> wrote:
> Dear Fieldtrippers,
> As a relative novice to classification algorithms, I have been playing around with Fieldtrip’s svm classifier (ft_statistics_crossvalidate). This seems to work pretty smoothly, but I have two questions related to that:
> 1.       In the given contingency table one can see the actual classification of trials. However, I assume the actual classification is based on a classification probability for each trial. Is there a way of getting a list of classification probabilities for all trials from this function? Else, a quick hint at where in the code these probabilities are being computed / stored would help me build this functionality
> 2.       I have EEG data that belong to 4 classes, but ft_statistics_crossvalidate does not afford multiclass approaches. Any tips or hints at what would be robust and well-validated approaches for multiclass models for EEG data would be much appreciated.
> Best,
> Marcel
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