Grael脑电放大器与深度学习的手势实时识别研究  

Real-time gesture recognition with Grael EEG amplifier and deep learning

作　　者：刘晓光[1,2] 张明进 王嘉威 梁铁[1,2] 李俊 刘秀玲[1,2] Liu Xiaoguang;Zhang Mingjin;Wang Jiawei;Liang Tie;Li Jun;Liu Xiuling(College of Electronic and Information Engineering,Hebei University,Baoding 071002,China;Key Laboratory of Digital Medical Engineering of Hebei Province,Hebei University,Baoding 071002,China)

机构地区：[1]河北大学电子信息工程学院,保定071002 [2]河北大学河北省数字医疗工程重点实验室,保定071002

出　　处：《电子测量技术》2023年第8期7-13,共7页Electronic Measurement Technology

基　　金：河北省自然科学基金(F2021201002,202101005);河北省教育厅重点项目(ZD2020146);保定市科技局项目(1911Q001)资助。

摘　　要：手势识别是人机交互的关键。为了能够更好地实现脑电信号与肌电信号的融合,精准地识别人体的运动,本文建立了一套基于Grael脑电放大器的手势动作实时检测识别的研究系统。通过Grael脑电放大器和Curry8系统采集5个通道的8种不同手势的表面肌电信号(sEMG),并对采集到的sEMG信号进行滤波去噪、滑动窗口分割以及特征提取等预处理的操作;最后采用几种常用的分类器与卷积神经网络(CNN)对不同手势的sEMG信号进行实时分类识别。结果表明CNN的识别准确率最高,能达到92.98%;对每个手势动作进行30次实时识别检测,结果显示识别延迟大概在1~1.5 s,实时识别的精度可高达90%。该系统为将来研究脑电信号与肌电信号的融合提供了一个可行的方法,在人机交互方面展现了巨大的潜力和应用空间。Gesture recognition is the key to human-computer interaction.In order to better realize the fusion of EEG and EMG signals and accurately recognize human motion,this paper established a research system of real-time gesture detection and recognition based on Grael EEG amplifier.The surface electromyography(sEMG)signals of eight different gestures from five channels were collected by Grael EEG amplifier and Curry8system,and the collected sEMG signals were preprocessed by filtering and denoising,sliding window segmentation and feature extraction.Finally,several commonly used classifiers and convolutional neural network(CNN)are used to classify and recognize sEMG signals of different gestures in real time.The results show that the recognition accuracy of CNN is the highest,reaching 92.98%.After 30times of real-time recognition and detection for each gesture,the results show that the recognition delay is about 1~1.5s,and the accuracy of real-time recognition can be up to 90%.This system provides a feasible method to study the fusion of EEG and EMG signals in the future,and shows great potential and application space in human-computer interaction.

关 键 词：Grael脑电放大器 表面肌电信号 手势识别 卷积神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]