EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network
Journal of Intelligent Information Systems
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
An MMG-based human-assisting manipulator using acceleration sensors
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Optimal design of a micro macro neural network to recognize rollover movement
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
On the recording reference contribution to EEG correlation, phase synchorony, and coherence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A muscular activation controlled rehabilitation robot system
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
A novel pattern classification method for multivariate EMG signals using neural network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A universal interface for video game machines using biological signals
ICEC'05 Proceedings of the 4th international conference on Entertainment Computing
Using speech rhythm knowledge to improve dysarthric speech recognition
International Journal of Speech Technology
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Proposes a new probabilistic neural network (NN) that can estimate the a-posteriori probability for a pattern classification problem. The structure of the proposed network is based on a statistical model composed by a mixture of log-linearized Gaussian components. However, the forward calculation and the backward learning rule can be defined in the same manner as the error backpropagation NN. In this paper, the proposed network is applied to the electroencephalogram (EEG) pattern classification problem. In the experiments described, two types of a photic stimulation, which are caused by eye opening/closing and artificial light, are used to collect the data to be classified. It is shown that the EEG signals can be classified successfully and that the classification rates change depending on the amount of training data and the dimension of the feature vectors