Abstract
This paper proposes a novel method to estimate the a posteriori probability for not belonging to predefined classes using probabilistic neural networks. By composing the Gaussian Mixture model and the one-versus-the-rest classifier model in the proposed network, any unlearned classes can be discriminated through training of each network using given learning samples. This method can be applied to EMG classification for various human-machine interfaces such as the prosthetic hand control system. EMG discrimination experiments were performed and the results showed that the method has a high performance for learned/not learned motion discrimination.
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