Exp Neurobiol.  2008 Dec;17(2):33-39. 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

• Lee Y
• Lee H
• Lang Y
• Kim J
• Lee M
• Shin HC

• KMID: 2165280
• DOI: http://doi.org/10.5607/en.2008.17.2.33

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.

Keyword

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

MeSH Terms

Aniline Compounds
Animals
Brain-Computer Interfaces
Hippocampus
Learning
Neural Prostheses
Neurons
Rats
Machine Learning
Aniline Compounds