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.
Highlights
A brain-machine interface (BMI) is a communication channel, which transforms a subject's thought processes into command signals to control various devices, for example, a computer application, a wheelchair, a robot arm, or a neural prosthesis
In order to avoid this issue and to obtain a more compact network architecture, this paper proposes an enhanced method for I-Extreme Learning Machine (ELM)
Spike trains of simultaneously recorded 34 single units for 10 min were used for ELM training (Figure 3) and those for another 10 min were used for testing purpose (Figure 4)
Summary
A brain-machine interface (BMI) is a communication channel, which transforms a subject's thought processes into command signals to control various devices, for example, a computer application, a wheelchair, a robot arm, or a neural prosthesis. Many studies have been made on the prediction of human voluntary movement intention in real-time based on invasive or noninvasive methods to help severely motor-disabled persons by offering some abilities of motor controls and communications. Advanced researches on invasive methods are being actively pursued with the aim of recovering complex and precise movements by decoding motor information in motor related brain areas [10,11]. Such researches have raised the hopes of paralyzed people. The number of motor-disabled and solitary aged people increases. A virtual reality linked to a general purpose BMI could be an alternative for the shortcoming resources on (page number not for citation purposes)
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