10:00 - 17:00 | Tue 30 Oct | Foyer | B1P-E
This paper proposes a selection method of motor imageries for brain-computer interfaces based on partial Kullback-Leibler information measure. In this method, partial KL information is defined as ratio of before:after class elimination and can be obtained by a KL information-based probabilistic neural network training. Therefore, optimal classes can be selected by eliminating ineffective ones one at a time along with network training. In the experiments performed, various motor imageries were learned by the reduced-dimensional recurrent probabilistic neural network and quasi-optimal combinations were selected using the proposed method. The discrimination rates before and after selections were 19.57±7.09 [%] and 68.14±21.70, respectively.
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