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why a 2 class classification is used in motor imagery
http://openvibe.inria.fr/forum/viewtopic.php?f=16&t=9834
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Author:  kiyarash [ Sat Oct 21, 2017 4:02 am ]
Post subject:  why a 2 class classification is used in motor imagery

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

Author:  jtlindgren [ Mon Oct 23, 2017 7:38 am ]
Post subject:  Re: why a 2 class classification is used in motor imagery

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

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