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Exp Neurobiol 2008; 17(2): 33-39
Published online December 31, 2008
© The Korean Society for Brain and Neural Sciences
Youngbum Lee1, Hyunjoo Lee2, Yiran Lang2, Jinkwon Kim1, Myoungho Lee1 and Hyung-Cheul Shin2*
1Department of Electrical & Electronic Engineering, Yonsei University, Seoul 120-752, 2Department of Physiology, College of Medicine, Hallym University, Chuncheon 200-702, Korea
Correspondence to: *To whom correspondence should be addressed.
TEL: 033-248-2585, FAX: 033-256-3426
A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n=34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.
Keywords: extreme learning machine, brain-machine interface, control commands, classification, neural prosthesis, neural population coding algorithm