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