Classification of BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals.
- Author:
Youngbum LEE
1
;
Hyunjoo LEE
;
Yiran LANG
;
Jinkwon KIM
;
Myoungho LEE
;
Hyung Cheul SHIN
Author Information
- Publication Type:Original Article
- Keywords: extreme learning machine; brain-machine interface; control commands; classification; neural prosthesis; neural population coding algorithm
- MeSH: Aniline Compounds; Animals; Brain-Computer Interfaces; Hippocampus; Learning; Neural Prostheses; Neurons; Rats; Machine Learning
- From:Experimental Neurobiology 2008;17(2):33-39
- CountryRepublic 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.
