基于高级控制策略的脑-机接口控制机械臂系统
|
摘要
目的为了增加脑-机接口(brain-computer interface,BCI)控制机械臂完成诸如抓取和放置的复杂操作的能力,本文设计与实现了一套新颖的基于脑-机接口,控制的机械臂系统。方法该系统主要包括计算机视觉、稳态视觉诱发电位脑-机接口和机械臂。计算机视觉用于识别工作区物体的形状和位置,低频稳态视觉诱发电位脑-机接口允许用户选择需要被操作的物体,机械臂则自主完成抓取和放置操作。为了验证机械臂系统,选取14名健康受试者,受试者均参加了离线实验,12名受试者参与在线实验。结果 12名健康受试者的在线结果表明,所构建的系统能够在6. 75 s内从4个可供选择的指令中输出一个命令,且获得(95. 24±1. 19)%的平均分类正确率。结论稳态视觉诱发电位的脑-机接口能够为机械臂提供精确、有效的高级控制。
Objective In order to increase the ability of brain-computer interface( BCI) to control a robotic arm to complete complex operations such as pick and place,this paper is designed and realized a novel brain-computer interface( BCI) controlled robotic arm. Methods The proposed system included computer vision,steady-state visual evoked potential( SSVEP)-based BCI,robotic arm. The computer vision could identify and locate objects in the workspace,the low-frequency SSVEP-based BCI allowed the user to select the objects that need to be operated. The robotic arm could autonomously pick and place the selected object. In order to verify the robotic arm system,14 healthy subjects were selected and all of them participated in the off-line test,12 subjects participated in the on-line test. Results Online results involving twelve subjects indicated that a command for the propose system could be selected from four possiblechoices in 6. 75 s with( 95. 24 ± 1. 19) % accuracy. Conclusions These results demonstrate an SSVEP-based BCI can provide accurate and efficient high-level control of a robotic arm.
Objective In order to increase the ability of brain-computer interface( BCI) to control a robotic arm to complete complex operations such as pick and place,this paper is designed and realized a novel brain-computer interface( BCI) controlled robotic arm. Methods The proposed system included computer vision,steady-state visual evoked potential( SSVEP)-based BCI,robotic arm. The computer vision could identify and locate objects in the workspace,the low-frequency SSVEP-based BCI allowed the user to select the objects that need to be operated. The robotic arm could autonomously pick and place the selected object. In order to verify the robotic arm system,14 healthy subjects were selected and all of them participated in the off-line test,12 subjects participated in the on-line test. Results Online results involving twelve subjects indicated that a command for the propose system could be selected from four possiblechoices in 6. 75 s with( 95. 24 ± 1. 19) % accuracy. Conclusions These results demonstrate an SSVEP-based BCI can provide accurate and efficient high-level control of a robotic arm.
引文
[1] Meng J, Zhang S, Bekyo A, et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks[J]. Scientific Reports,2016,6:38565.
[2] Wolpaw JR,Birbaumer N,Mc Farland DJ,et al. Brain-computer interfaces for communication and control[J]. Clinical Neurophysiology,2002,113:767-791.
[3] Chen X,Wang Y,Nakanishi M,et al. High-speed spelling with a noninvasive brain-computer interface[J]. Proceedings ofthe National Academy of Sciences of the United States of America,2015,112(44):E6058-E6067.
[4] Allison BZ,Brunner C,Altsttter C,et al. A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control[J].Journal of Neuroscience Methods,2012,209(2):299-307.
[5] Chen X,Zhao B,Wang Y,et al. Control of a 7-DOF robotic arm system with an SSVEP-based BCI[J]. International Journal of Neural Systems,2018,28(8):1850018.
[6] Novak D,Sigrist R,Gerig NJ,et al. Benchmarking braincomputer interfaces outside the laboratory:the Cybathlon 2016[J].Frontiers in Neuroscience,2018,11:756.
[7]陈小刚,王毅军.基于脑电的无创脑机接口研究进展[J].科技导报,2018,36(12):22-30.Chen X, Wang Y. A review of non-invasive electroencephalogram-based brain computer interfaces[J].Science&Technology Review,2018,36(12):22-30.
[8] Yu T,Xiao J,Wang F,et al. Enhanced motor imagery training using a hybrid BCI with feedback[J]. IEEE Transactions on Biomedical Engineering,2015,62(7):1706-1717.
[9] Zhang Y,Nam CS,Zhou G,et al. Temporally constrained sparse group spatial patterns for motor imagery BCI[J]. IEEE Transactions on Cybernetics,2018,6:1-11.
[10] Jin J,Zhang H,Daly I,et al. An improved P300 pattern in BCI to catch user’s attention[J]. Journal of Neural Engineering,2017,14(3):036001.
[11] Xu M,Qi H,Ma L,et al. Channel selection based on phase measurement in P300-based brain-computer interface[J]. Plos One,2013,8(4):e60608.
[12] Chen X,Wang Y,Gao S,et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based braincomputer interface[J]. Journal of Neural Engineering,2015,12(4):046008.
[13] Chen X,Chen Z,Gao S,et al. A high-ITR SSVEP based BCI speller[J]. Brain-Computer Interfaces,2014,1:181-191.
[14] Hortal E,Ianez E,Ubeda A,et al. Combining a brain-machine interface and an electrooculography interface to perform pick and place tasks with a robotic arm[J]. Robotics and Autonomous Systems,2015,72:181-188.
[15] Bi L,Fan X A,Liu Y. EEG-based brain-controlled mobile robots:a survey[J]. IEEE Transactions on Human-Machine Systems,2013,43(2):161-176.
[16] Bin G,Gao X,Yan Z,et al. An online multi-channel SSVEPbased brain-computer interface using a canonical correlation analysis method[J].Journal of Neural Engineering,2009,6(4):046002.
[17] Nakanishi M,Wang Y,Chen X,et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis[J]. IEEE Transactions on Biomedical Engineering,2018,65(1):104-112.
[2] Wolpaw JR,Birbaumer N,Mc Farland DJ,et al. Brain-computer interfaces for communication and control[J]. Clinical Neurophysiology,2002,113:767-791.
[3] Chen X,Wang Y,Nakanishi M,et al. High-speed spelling with a noninvasive brain-computer interface[J]. Proceedings ofthe National Academy of Sciences of the United States of America,2015,112(44):E6058-E6067.
[4] Allison BZ,Brunner C,Altsttter C,et al. A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control[J].Journal of Neuroscience Methods,2012,209(2):299-307.
[5] Chen X,Zhao B,Wang Y,et al. Control of a 7-DOF robotic arm system with an SSVEP-based BCI[J]. International Journal of Neural Systems,2018,28(8):1850018.
[6] Novak D,Sigrist R,Gerig NJ,et al. Benchmarking braincomputer interfaces outside the laboratory:the Cybathlon 2016[J].Frontiers in Neuroscience,2018,11:756.
[7]陈小刚,王毅军.基于脑电的无创脑机接口研究进展[J].科技导报,2018,36(12):22-30.Chen X, Wang Y. A review of non-invasive electroencephalogram-based brain computer interfaces[J].Science&Technology Review,2018,36(12):22-30.
[8] Yu T,Xiao J,Wang F,et al. Enhanced motor imagery training using a hybrid BCI with feedback[J]. IEEE Transactions on Biomedical Engineering,2015,62(7):1706-1717.
[9] Zhang Y,Nam CS,Zhou G,et al. Temporally constrained sparse group spatial patterns for motor imagery BCI[J]. IEEE Transactions on Cybernetics,2018,6:1-11.
[10] Jin J,Zhang H,Daly I,et al. An improved P300 pattern in BCI to catch user’s attention[J]. Journal of Neural Engineering,2017,14(3):036001.
[11] Xu M,Qi H,Ma L,et al. Channel selection based on phase measurement in P300-based brain-computer interface[J]. Plos One,2013,8(4):e60608.
[12] Chen X,Wang Y,Gao S,et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based braincomputer interface[J]. Journal of Neural Engineering,2015,12(4):046008.
[13] Chen X,Chen Z,Gao S,et al. A high-ITR SSVEP based BCI speller[J]. Brain-Computer Interfaces,2014,1:181-191.
[14] Hortal E,Ianez E,Ubeda A,et al. Combining a brain-machine interface and an electrooculography interface to perform pick and place tasks with a robotic arm[J]. Robotics and Autonomous Systems,2015,72:181-188.
[15] Bi L,Fan X A,Liu Y. EEG-based brain-controlled mobile robots:a survey[J]. IEEE Transactions on Human-Machine Systems,2013,43(2):161-176.
[16] Bin G,Gao X,Yan Z,et al. An online multi-channel SSVEPbased brain-computer interface using a canonical correlation analysis method[J].Journal of Neural Engineering,2009,6(4):046002.
[17] Nakanishi M,Wang Y,Chen X,et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis[J]. IEEE Transactions on Biomedical Engineering,2018,65(1):104-112.