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1

Graduate School of Science and Engineering for Education, University of Toyama, Gofuku Campus, 3190 Gofuku, Toyama 930-8555, Japan

2

Faculty of Engineering, University of Toyama, Gofuku Campus, 3190 Gofuku, Toyama 930-8555, Japan

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Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259-G5-17, Yokohama-shi, Kanagawa 226-8503, Japan

4

Department of Life Sciences and Bioengineering, University of Toyama, Gofuku Campus, 3190 Gofuku, Toyama 930-8555, Japan





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Abstract Brain machine interface BMI has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks RBFNN outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position. View Full-Text

Keywords: brain machine interface; learning and adaptive systems; radial basis function neural controllers brain machine interface; learning and adaptive systems; radial basis function neural controllers





Autor: Marsel Mano 1,* , Genci Capi 2, Norifumi Tanaka 3 and Shigenori Kawahara 4

Fuente: http://mdpi.com/



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