留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于稳态视觉诱发电位的脑控轮椅室内模拟训练系统

王金海 王康宁 陈小刚 王慧泉 徐圣普 刘明

王金海, 王康宁, 陈小刚, 王慧泉, 徐圣普, 刘明. 基于稳态视觉诱发电位的脑控轮椅室内模拟训练系统[J]. 机械工程学报, 2020, 37(3): 502-511. doi: 10.7507/1001-5515.201906025
引用本文: 王金海, 王康宁, 陈小刚, 王慧泉, 徐圣普, 刘明. 基于稳态视觉诱发电位的脑控轮椅室内模拟训练系统[J]. 机械工程学报, 2020, 37(3): 502-511. doi: 10.7507/1001-5515.201906025
Jinhai WANG, Kangning WANG, Xiaogang CHEN, Huiquan WANG, Shengpu XU, Ming LIU. Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 502-511. doi: 10.7507/1001-5515.201906025
Citation: Jinhai WANG, Kangning WANG, Xiaogang CHEN, Huiquan WANG, Shengpu XU, Ming LIU. Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials[J]. JOURNAL OF MECHANICAL ENGINEERING, 2020, 37(3): 502-511. doi: 10.7507/1001-5515.201906025

基于稳态视觉诱发电位的脑控轮椅室内模拟训练系统

doi: 10.7507/1001-5515.201906025
详细信息
    通讯作者:

    刘明,Email:liuming_tju@163.com

Indoor simulation training system for brain-controlled wheelchair based on steady-state visual evoked potentials

More Information
  • 摘要: 脑控轮椅(BCW)是脑-机接口技术的重要应用之一,目前研究表明,模拟控制训练对 BCW 应用具有重要意义。为了在保证安全的情况下提高使用者的 BCW 控制能力,达到促进 BCW 应用的目的,本文基于稳态视觉诱发电位搭建了 BCW 室内模拟训练系统。该系统包括视觉刺激范式设计与实现、脑电信号采集与处理、室内模拟环境建模、路径规划和模拟轮椅控制等。为测试该系统性能,本研究设计了三种室内路径控制任务,共招募 10 名受试者进行为期 5 d 的训练试验。比较训练试验前后结果发现,受试者完成任务一、任务二和任务三时,所用平均命令个数分别下降 29.5%、21.4%、25.4%(P < 0.001),整体所用命令个数平均下降 25.4%( P < 0.001)。试验结果表明,通过本文搭建的室内模拟训练系统对受试者进行训练,能够在一定程度上提高受试者对 BCW 控制的熟练度和操控效率,证明了本文系统的实用性,为促进 BCW 室内应用提供了一个有效的辅助方法。

     

  • 图  模拟训练系统框图

    Figure  1.  The block diagram of the simulation training system

    图  家居环境及模拟环境示意图

    Figure  2.  The schematic diagrams of home environment and simulation environment

    图  A*算法流程图

    Figure  3.  Flow chart of A* algorithm

    图  SSVEP 刺激时序图

    Figure  4.  Sequence diagram of SSVEP stimulation

    图  试验任务路线和试验场景

    Figure  5.  Schematic diagram of the experimental task path and experimental scenario

    图  单次训练组块流程示意图

    Figure  6.  Schematic diagram of a single training block

    图  各任务平均命令个数分布

    Figure  7.  Distribution of the average number of commands for each task

    表  1  模拟训练试验结果

    Table  1.   The results of the simulation training experiment

    受试者 任务 平均用时/s( $\overline {{x}} \;{\text{±}}\; {{s}}$ 平均命令/个( $\overline {{x}} \;{\text{±}}\; {{s}}$ 碰撞/次 误启动/次 无效命令/个 平均时间优化率
    S1 376.00 ± 39.76 94.00 ± 9.94 0 0 19 1.21
    358.68 ± 11.48 89.67 ± 2.87 0 0 5 1.08
    342.24 ± 28.36 85.56 ± 7.09 0 2 7 1.14
    S2 441.76 ± 81.64 110.44 ± 20.41 2 0 13 1.42
    420.00 ± 57.36 105.00 ± 14.34 3 0 23 1.27
    370.68 ± 49.12 92.67 ± 12.28 1 0 7 1.24
    S3 370.24 ± 25.92 92.56 ± 6.48 1 1 16 1.19
    378.24 ± 26.92 94.56 ± 6.73 0 0 15 1.14
    351.12 ± 26.96 87.78 ± 6.74 0 0 14 1.17
    S4 380.44 ± 22.40 95.11 ± 5.60 0 0 15 1.22
    388.44 ± 33.44 97.11 ± 8.36 1 0 26 1.17
    357.32 ± 25.12 89.33 ± 6.28 1 0 32 1.19
    S5 376.00 ± 48.48 94.00 ± 12.12 3 1 12 1.21
    389.32 ± 27.72 97.33 ± 6.93 3 1 8 1.17
    340.44 ± 20.04 85.11 ± 5.01 2 1 10 1.13
    S6 400.44 ± 42.92 100.11 ± 10.73 2 1 8 1.28
    404.44 ± 18.36 101.11 ± 4.59 1 0 25 1.22
    368.00 ± 43.08 92.00 ± 10.77 3 0 13 1.23
    S7 406.24 ± 47.28 101.56 ± 11.82 2 0 20 1.30
    417.32 ± 45.48 104.33 ± 11.37 5 1 30 1.26
    383.56 ± 47.88 95.89 ± 11.97 3 0 16 1.28
    S8 408.88 ± 69.44 102.22 ± 17.36 2 0 14 1.31
    423.12 ± 39.32 105.78 ± 9.83 3 0 31 1.27
    401.32 ± 53.12 100.33 ± 13.28 3 0 25 1.34
    S9 415.56 ± 78.76 103.89 ± 19.69 5 0 10 1.33
    408.44 ± 50.04 102.11 ± 12.51 2 0 15 1.23
    385.32 ± 48.92 96.33 ± 12.23 0 0 6 1.28
    S10 400.44 ± 60.72 100.11 ± 15.18 4 0 19 1.28
    405.32 ± 65.88 101.33 ± 16.47 3 0 18 1.22
    373.76 ± 56.60 93.44 ± 14.15 1 0 14 1.25
    下载: 导出CSV

    表  2  E1、E2 和 E3 的平均命令个数表( ${\overline {{x}} \;{b\text{±}}\; {{s}}}$

    Table  2.   The average number of commands for E1,E2,and E3( ${\overline {{x}} \;{b\text{±}}\; {{s}}}$

    试验
    序号
    平均命令数/个
    S1 S2 S3 S4 S5 S6 S7 S8 S9 S10
    E1 93.44 ± 7.28 120.22 ± 16.84 98.67 ± 4.92 98.78 ± 8.00 99.78 ± 10.40 104.89 ± 9.60 113.44 ± 10.14 116.44 ± 11.44 115.78 ± 10.30 117.33 ± 6.28
    E2 93.78 ± 4.58 97.33 ± 9.18 90.67 ± 5.10 93.56 ± 5.88 89.11 ± 6.64 97.00 ± 8.05 96.22 ± 5.21 100.56 ± 8.14 102.78 ± 6.82 91.89 ± 6.77
    E3 82.00 ± 4.72 90.56 ± 6.91 85.56 ± 3.40 89.22 ± 5.24 87.56 ± 7.63 91.33 ± 6.87 92.11 ± 5.99 91.33 ± 5.61 83.78 ± 4.41 85.67 ± 4.87
    下载: 导出CSV

    表  3  模拟训练试验问卷综合结果

    Table  3.   The comprehensive results of the questionnaire in the simulation training experiment

    评价指标 E1 E2 E3
    脑力需求 较低 较低
    身体负担 较低
    时间需求 中等 中等 较低
    挫败感 较低
    下载: 导出CSV
  • [1] Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2): 164-173. doi: 10.1109/TRE.2000.847807
    [2] Tanaka K, Matsunaga K, Wang H O. Electroencephalogram-based control of an electric wheelchair. IEEE Trans Robot, 2005, 21(4): 762-766. doi: 10.1109/TRO.2004.842350
    [3] Yu Yang, Liu Yadong, Jiang Jun, et al. An asynchronous control paradigm based on sequential motor imagery and its application in wheelchair navigation. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(12): 2367-2375. doi: 10.1109/TNSRE.2018.2881215
    [4] Rebsamen B, Guan C, Zhang H, et al. A brain controlled wheelchair to navigate in familiar environments. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(6): 590-598. doi: 10.1109/TNSRE.2010.2049862
    [5] Tang Jingsheng, Liu Yadong, Hu Dewen, et al. Towards BCI-actuated smart wheelchair system. Biomed Eng Online, 2018, 17(1): 111. doi: 10.1186/s12938-018-0545-x
    [6] Diez P F, Müller S T, Mut V A, et al. Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface. Med Eng Phys, 2013, 35(8): 1155-1164. doi: 10.1016/j.medengphy.2012.12.005
    [7] Müller S T, Celeste W C, Bastos-Filho T F. Brain-computer interface based on visual evoked potentials to command autonomous robotic wheelchair. J Med Biol Eng, 2010, 30(6): 407-416. doi: 10.5405/jmbe.765
    [8] Long Jinyi, Li Yuanqing, Wang Hongtao, et al. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng, 2012, 20(5): 720-729. doi: 10.1109/TNSRE.2012.2197221
    [9] Yu Yang, Zhou Zongtan, Liu Yadong, et al. Self-paced operation of a wheelchair based on a hybrid brain-computer interface combining motor imagery and P300 potential. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(12): 2516-2526. doi: 10.1109/TNSRE.2017.2766365
    [10] Bi Luzheng, Fan Xinan, Liu Yili. EEG-based brain-controlled mobile robots: a survey. IEEE T Hum-Mach Syst, 2013, 43(2): 161-176. doi: 10.1109/TSMCC.2012.2219046
    [11] Leeb R, Friedman D, Müller-Putz G R, et al. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci, 2007(2): 79642.
    [12] Gentiletti G G, Gebhart J G, Acevedo R C, et al. Command of a simulated wheelchair on a virtual environment using a brain-computer interface. IRBM, 2009, 30(5): 218-225.
    [13] Huang Dandan, Qian Kai, Fei Dingyu, et al. Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans Neural Syst Rehabil Eng, 2012, 20(3): 379-388. doi: 10.1109/TNSRE.2012.2190299
    [14] Velasco-Álvarez F, Ron-Angevin R, Silva-Sauer L D, et al. Audio-cued motor imagery-based brain-computer interface: navigation through virtual and real environments. Neurocomputing, 2013, 121: 89-98. doi: 10.1016/j.neucom.2012.11.038
    [15] Herweg A, Gutzeit J, Kleih S, et al. Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol Psychol, 2016, 121(Pt A): 117-124.
    [16] Yu Tianyou, Xiao Jun, Wang Fangyi, et al. Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Trans Biomed Eng, 2015, 62(7): 1706-1717. doi: 10.1109/TBME.2015.2402283
    [17] Baykara E, Ruf C A, Fioravanti C, et al. Effects of training and motivation on auditory P300 brain-computer interface performance. Clin Neurophysiol, 2016, 127(1): 379-387. doi: 10.1016/j.clinph.2015.04.054
    [18] Wan Feng, Cruz J D, Nan Wenya, et al. Alpha neurofeedback training improves SSVEP-based BCI performance. J Neural Eng, 2016, 13(3): 036019. doi: 10.1088/1741-2560/13/3/036019
    [19] Rose F D, Attree E A, Brooks B M, et al. Training in virtual environments: transfer to real world tasks and equivalence to real task training. Ergonomics, 2000, 43(4): 494-511. doi: 10.1080/001401300184378
    [20] Kenyon R V, Afenya M B. Training in virtual and real environments. Ann Biomed Eng, 1995, 23(4): 445-455. doi: 10.1007/BF02584444
    [21] Holden M K. Virtual environments for motor rehabilitation: review. Cyberpsychol Behav, 2005, 8(3): 187-211. doi: 10.1089/cpb.2005.8.187
    [22] Todorov E, Shadmehr R, Bizzi E. Augmented feedback presented in a virtual environment accelerates learning of a difficult motor task. J Mot Behav, 1997, 29(2): 147-158. doi: 10.1080/00222899709600829
    [23] Vialatte F B, Maurice M, Dauwels J, et al. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog Neurobiol, 2010, 90(4): 418-438. doi: 10.1016/j.pneurobio.2009.11.005
    [24] Chen Xiaogang, Chen Zhikai, Gao Shangkai, et al. A high-ITR SSVEP-based BCI speller. Brain-Computer Interfaces, 2014, 1(3-4): 181-191. doi: 10.1080/2326263X.2014.944469
    [25] Pastor M A, Artieda J, Arbizu J, et al. Human cerebral activation during steady-state visual-evoked responses. J Neurosci, 2003, 23(37): 11621-11627. doi: 10.1523/JNEUROSCI.23-37-11621.2003
    [26] Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12(4): 046008. doi: 10.1088/1741-2560/12/4/046008
    [27] Lin Zhonglin, Zhang Changshui, Wu Wei, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng, 2007, 54(6): 1172-1176. doi: 10.1109/TBME.2006.889197
    [28] 陈小刚, 赵秉, 刘明, 等. 稳态视觉诱发电位脑-机接口控制机械臂系统的设计与实现. 生物医学工程与临床, 2018, 22(3): 20-26.
    [29] 陈若男, 文聪聪, 彭玲, 等. 改进 A*算法在机器人室内路径规划中的应用. 计算机应用, 2019, 39(4): 1006-1011. doi: 10.11772/j.issn.1001-9081.2018091977
    [30] Iturrate I, Antelis J M, Kubler A, et al. A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation. IEEE Trans Robot, 2009, 25(3): 614-627. doi: 10.1109/TRO.2009.2020347
  • 加载中
图(7) / 表(3)
计量
  • 文章访问数:  1239
  • HTML全文浏览量:  209
  • PDF下载量:  1
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-06-11
  • 修回日期:  2020-01-19
  • 发布日期:  2020-03-17

目录

    /

    返回文章
    返回