Development of A Control System of Neural Prosthesis Hand Driven by BCI

本文针对现代医疗手段还无法使上臂再生的问题,深入分析了人手动作脑电信号的特点,研究了面向义肢手控制的手动作脑电小波特征提取和BP神经网络模式识别方法,开发研制了一个基于BCI(Brain-Computer Interface,简称BCI)驱动的神经义肢手驱动控制系统,并用该系统完成了义肢手四种动作(手臂自由状态、手臂移动、手抓取、手张开)的驱动。经过多次在线及离线实验,结果表明:基于脑-机接口驱动的神经义肢手系统是合理可行的,所采用的脑电信号小波特征提取方法和BP神经网络模式识别方法是有效的

Design of a grasp force adaptive control system with tactile and slip perception

This paper realizes the tactile and slip perception of prostheses finger by using of PVDF (Polyvinylidene Fluoride) piezoelectric membrane. The mathematics model of brushless DC motor is constructed and the characteristics of PVDF tactile and slipping sensor are analyzed. The PID controller is adaptive by designing the control system with tactile and slip feedback. The feedback system implements adaptive control. The simulink dynamic model of the control system is modeled. The simulation results show the control system can satisfy the requirements and has good response characteristics

Design of wearable intelligent mind controlled prosthetic hand

This paper presented the prosthetic humanoid operation model which is controlled by the brain. According to the humanoid operated mechanism, the brain signals detection, intelligent prosthetic control, and the man-machine cooperation prosthetic model were studied. The wearable intelligent mind controlled prosthetic hand (IMCPHand) was designed with integration of multi-point tactile and slippery sensors in the prosthetic space and three-dimensional acceleration sensor information fusion. And also a method based on Hidden Markov Model for IMCPHand's man-machine cooperation control addressed which provided a feasible way for the IMCPHand completing more complex tasks autonomously

An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive

For controlling the prosthetic hand by only electroencephalogram (EEG), it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm's free state, arm movement, hand crawl, hand open). Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiersbased method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies