An improved adaptive RBF network for classification of left and right hand motor imagery tasks

Authors:
Xiao-mei Pei;Jin Xu;Chong-xun Zheng;Guang-yu Bin
Affiliations:
Institute of Biomedical Engineering of Xi'an Jiaotong University, Xi'an, China;Institute of Biomedical Engineering of Xi'an Jiaotong University, Xi'an, China;Institute of Biomedical Engineering of Xi'an Jiaotong University, Xi'an, China;Institute of Biomedical Engineering of Xi'an Jiaotong University, Xi'an, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

An improved adaptive RBF neural network is proposed to realize the continuous classification of left and right hand motor imagery tasks. Leader-follower clustering is used to initialize the centers and variances of hidden layer neurons, which matches the time-variant input features. Based on the features of multichannel EEG complexity and field power, the time courses of two evaluating indexes i.e. classification accuracy and mutual information (MI) are calculated to obtain the maximum with 87.14% and 0.53bit respectively. The results show that the improved algorithm can provide the flexible initial centers of RBF neural network and could be considered for the continuous classification of mental tasks for BCI (Brain Computer Interface) application.