Normalizing signal or feature streams
Posted: Tue Dec 29, 2015 7:47 pm
Hi OpenVibers,
The SVM algorithm (libsvm library) seems to perform very poorly on the standard motor imagery scenario bundled with OV (the one with a preliminary SCP filtering)... versus the standard LDA (Linear Discriminant Analysis): it appears that the feature vectors required by the SVM classifier have to be normalized. Have any of you reached satisfactory results with the SVM classifier ?
I was wondering how to normalize (X transformed into (X-mean(X))/sqrt(var(X)) variables on OV ? Some contributors suggest to use the "simple DSP" box but I don't see how to add inputs on it to inject the Mean and Variance signals computed by the "Univariate Statistics" box. Is there a way to do this ? (without having to create the new box "Normalizer")
The SVM algorithm (libsvm library) seems to perform very poorly on the standard motor imagery scenario bundled with OV (the one with a preliminary SCP filtering)... versus the standard LDA (Linear Discriminant Analysis): it appears that the feature vectors required by the SVM classifier have to be normalized. Have any of you reached satisfactory results with the SVM classifier ?
I was wondering how to normalize (X transformed into (X-mean(X))/sqrt(var(X)) variables on OV ? Some contributors suggest to use the "simple DSP" box but I don't see how to add inputs on it to inject the Mean and Variance signals computed by the "Univariate Statistics" box. Is there a way to do this ? (without having to create the new box "Normalizer")