why a 2 class classification is used in motor imagery

About BCI and box tutorial/demo scenarios bundled with OpenViBE.
Post Reply
kiyarash
Posts: 27
Joined: Tue Apr 11, 2017 10:44 am

why a 2 class classification is used in motor imagery

Post by kiyarash »

in the motor imagery csp scenario provided we want to detect right or left hand imagiation using the algorithm. shouldn't it use a 3 class classification since there are 3 states : imagining right hand movement, imagining left hand movement and also imagining nothing.

wouldn't this make the scenario work inaccurately?

thanks
kiyarash

jtlindgren
Posts: 778
Joined: Tue Dec 04, 2012 3:53 pm
Location: INRIA Rennes, FRANCE

Re: why a 2 class classification is used in motor imagery

Post by jtlindgren »

Hi,

some research papers use an additional 'nothing' class, some do not. The Graz paradigm in openvibe is defined as two class; since during training and testing it is *assumed* that the user is a good boy/girl/person and obediently imagines of only left or right (as instructed) during the trial, this limited context is not requiring a third class. From real use perspective (for example controlling something), a third class of nothing could be useful. Adding a third class will also change the classification problem in a machine learning sense. How it will change it is doubtlessly a research question in itself. If anybody knows related links, feel free to post. :)


Cheers,
Jussi

Post Reply