Experimental Neurobiology  2008;17(2):33-39

doi:10.5607/en.2008.17.2.33

Classification of BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals.

Youngbum LEE 1 ; Hyunjoo LEE ; Yiran LANG ; Jinkwon KIM ; Myoungho LEE ; Hyung Cheul SHIN

Affiliations

+expand

Keywords

extreme learning machine; brain-machine interface; control commands; classification; neural prosthesis; neural population coding algorithm

Country

Republic of Korea

Language

English

Abstract

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.