RP-based Voluntary Movement Intention Detection of Lower limb using CNN

Abstract

Exoskeleton robots and brain computer interface (BCI) have a considerable development. But, there is a problem of relatively lower detection accuracy for voluntary movement intention. Deep learning is an effective method to solve it, hence a RP-based voluntary movement intention detection of lower limb using convolution neural network (CNN) is proposed in this paper. Firstly, the mechanism of readiness potential (RP) was analyzed, its property is an important basis for movement intention detection. Then, the lower limb voluntary movement intention detection system based on electroencephalogram (EEG) was established and six healthy subjects participated in this experiment. The different CNN models were established as a classifier. The experimental results show that the window length of EEG classification data was set as 300ms to get a better detection accuracy and time performance. The average detection accuracy of right leg voluntary movement using AlexNet, VGGNet-16 and VGGNet-19 was 72.1%, 83.0% and 84.2% respectively. Specifically, the results suggested that the detection accuracy was also increased with the increase of CNN network layer. This work demonstrates that VGGNet-based CNN model can detect lower limb voluntary movement intention, it lay a foundation for human-computer interaction of lower limb exoskeleton robot.