[Mne_analysis] CSP for Multi-Classification

Brunner, Clemens (clemens.brunner@uni-graz.at) clemens.brunner at uni-graz.at
Sat Feb 23 17:20:08 EST 2019
Search archives:

        External Email - Use Caution        


Could you please be more specific? What have you tried for the multi-class case (how many classes?) and which method yielded better results? Which performance metric did you consider? Which data (cross-validation)? CSP is a spatial filter, and you can train filters e.g. in a 1-vs-rest scheme.


From: mne_analysis-bounces at nmr.mgh.harvard.edu <mne_analysis-bounces at nmr.mgh.harvard.edu> On Behalf Of tr rt
Sent: Saturday, 23 February 2019 11:59
To: mne_analysis at nmr.mgh.harvard.edu
Subject: [Mne_analysis] CSP for Multi-Classification

        External Email - Use Caution
Hi everyone,
CSP (from mne.decoding import CSP) (Common Spatial Patterns) for Multi-Classification gives clearly worse accuracy than the binary classification. Would you suggest a better solution for the case of more than two classes? Maybe another kind of CSP? Another kind of Machine Learning methods? or even Deep Learning?
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20190223/5bdc9743/attachment.html 

More information about the Mne_analysis mailing list