Title: A Master-Slave Neural Network for precise recognition of the complicated hand operations based on EEG
Authors: Xiao Dong Zhang, Hyouk Ryeol Choi
Addresses: School of Engine and Energy, Northwestern Polytechnical University, 127 Youyi West Road, Xi'an, Shaanxi Province 710072, China. ' School of Mechanical Engineering, Sungkyunkwan University, 300 Chunchun-dong, Jangan-gu, Suwon, 440-746, Korea
Abstract: On the basis of excellent features of the Hopfield neural network, a new Master-Slave Neural Network (simply denoted as MSNN) model was presented in this paper. The structure of the proposed MSNN was first designed, and the corresponding training algorithm was discussed in detail, and the stability of the MSNN was analysed in detail. Finally, through a two-channel EEG measurement system set-up, and the feature of the related EEG signals extracted, some complicated hand operations were recognised by using the MSNN and BP neural network. The comparison showed that the MSNN had a better asymptotic convergence rate and a higher mapping precision, so that a higher recognition possibility was achieved than the BP network. This is an expanded version of a paper presented at the 3rd IEEE International Workshop on Medical Measurements and Applications, 9–10 May 2008, Ottawa, ON, Canada.
Keywords: artificial neural networks; ANNs; pattern recognition; hand operations; EEG signals; electroencephalography; stability; EEG measurements.
International Journal of Advanced Media and Communication, 2009 Vol.3 No.1/2, pp.55 - 79
Published online: 27 Jun 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article