基于SSVEP与运动想象的实时脑控阿凡达系统

doi:10.3969/j.issn.1006-2475.2018.01.008

计算机与现代化 ›› 2018, Vol. 0 ›› Issue (01): 36-39.doi: 10.3969/j.issn.1006-2475.2018.01.008

基于SSVEP与运动想象的实时脑控阿凡达系统

广东工业大学自动化学院，广东-广州-510006

收稿日期:2017-05-03 出版日期:2018-01-23 发布日期:2018-01-24
作者简介:林铭铎（1995-），男，广东揭阳人，广东工业大学自动化学院本科生，研究方向：脑电信号处理，机器学习；欧祖宏（1996-），男，广东广宁人，本科生，研究方向：脑机接口。

Real-time Brain-controlled Avatar System Based on SSVEP and Motor Imagery

School of Automation， Guangdong University of Technology， Guangzhou 510006， China

Received:2017-05-03 Online:2018-01-23 Published:2018-01-24

摘要/Abstract

摘要： 针对当前在线脑机接口交互系统识别正确率比较低、模式单一、算法复杂度高等问题，设计基于SSVEP和运动想象的实时脑控实时算法并应用于脑控阿凡达系统。通过对6名受试者进行离线训练和在线测试，实验结果表明该系统较好地反映受试者的控制意图，所有受试者均可以基本准确地控制机器人，可实现多指令的控制，识别准确率高，可进一步推动脑机接口在实际领域的应用和发展。

关键词: 稳态视觉诱发, 运动想象, 脑机接口, 脑控阿凡达

Abstract: Aiming at solving the problem that the accuracy rate of recognition system is relatively low, the model is simple and the complexity of the algorithm is high, the real-time brain control real-time algorithm based on SSVEP and motion imagination is designed and applied to the brain-controlled Avatar system. By conducting off-line training and online testing of six subjects, the experimental results show that the system better reflects the control intention of the subject. All subjects can basically control the robot and achieve multi-instruction control with high recognition accuracy. The system steps to promote the brain-computer interface in the practical application and development.

Key words: motor imagery, brain-computer interface, brain-controlled Avatar

林铭铎，欧祖宏. 基于SSVEP与运动想象的实时脑控阿凡达系统[J]. 计算机与现代化, 2018, 0(01): 36-39.

LIN Ming-duo, OU Zu-hong. Real-time Brain-controlled Avatar System Based on SSVEP and Motor Imagery[J]. Computer and Modernization, 2018, 0(01): 36-39.

参考文献

［1］He Bin, Gao Shangkai, Yuan Han, et al. Brain-computer interfaces［M］// Neural Engineering. Springer US, 2013:87-151.
［2］Pfurtscheller G, Da Silva F H L. Event-related EEG/MEG synchronization and desynchronization: Basic principles［J］. Clinical Neurophysiology, 1999,110(11):1842-1857.
［3］LaFleur K, Cassady K, Doud A, et al. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface［J］. Journal of Neural Engineering, 2013,10(4):046003.
［4］Meng Jianjun, Zhang Shuying, Bekyo A, et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks［J］. Scientific Reports, 2016,6:38565.
［5］Farwell L A, Donchin E. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials［J］. Electroencephalography and Clinical Neurophysiology, 1988,70(6):510-523.
［6］Donchin E, Spencer K M, Wijesinghe R. The mental prosthesis: Assessing the speed of a P300-based brain-computer interface［J］. IEEE Transactions on Rehabilitation Engineering, 2000,8(2):174-179.
［7］Soutton S, Braren M, Zubin J, et al. Infonnation delivery and the sensory evoked potential［J］. Science, 1965,155(3768):1436-1439.
［8］Allison B, Faller J, Neuper C H. BCIs that use steady state visual evoked potentials or slow cortical potentials［M］// Brain-Computer Interfaces: Principles and Practice. Oxford University Press, 2012.
［9］Vidal J J. Toward direct brain-computer communication［J］. Annual review of Biophysics and Bioengineering, 1973,2(1):157-180.
［10］Birbaumer N, Ghanayim N, Hinterberger T, et al. A spelling device for the paralysed［J］. Nature, 1999,398(6725):297-298.
［11］Panicker R C, Puthusserypady S, Sun Y. An asynchronous P300 BCI with SSVEP-based control state detection［J］. IEEE Transactions on Biomedical Engineering, 2011,58(6):1781-1788.
［12］Cheng Ming, Gao Xian, Gao Xiaorong, et al. Design and implementation of a brain-computer interface with high transfer rates［J］. IEEE Transactions on Biomedical Engineering, 2002,49(10):1181-1186.
［13］Wang Yijun, Wang Ruiping, Gao Lian, et al. A practical VEP-based brain-computer interface［J］. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006,14(2):234-240.
［14］Gao Xiaorong, Xu Dingfeng, Cheng Ming, et al. A BCI-based environmental controller for the motion-disabled［J］. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003,11(2):137-140.
［15］Birbaumer N, Kubler A, Ghanayim N, et al. The thought translation device (TTD) for completely paralyzed patients［J］. IEEE Transactions on Rehabilitation Engineering, 2000,8(2):190-193.
［16］Birbaumer N, Elbert T, Canavan A G, et al. Slow potentials of the cerebral cortex and behavior［J］. Physiological Reviews, 1990,70(1):1-41.
［17］Sanei S, Chambers J A. EEG Signal Processing［M］. John Wiley & Sons, 2013.
［18］Pastor M A, Artieda J, Arbizu J, et al. Human cerebral activation during steady-state visual-evoked responses［J］. Journal of Neuroscience, 2003,23(37):11621-11627.

[1]	周成诚, 曾庆军, 杨康, 胡家铭, 韩春伟. 基于高效通道注意力模块的运动想象脑电识别[J]. 计算机与现代化, 2023, 0(12): 19-23.
[2]	莫云. 基于混合特征选择的脑电解码方法[J]. 计算机与现代化, 2022, 0(04): 92-96.
[3]	黄旭彬, 梁树杰. 基于PCA融合PSO-SVM的运动想象脑电信号分类方法[J]. 计算机与现代化, 2021, 0(03): 70-76.
[4]	黄旭彬, 张金霜. 基于运动想象脑电控制的智能家居系统[J]. 计算机与现代化, 2021, 0(02): 68-72.
[5]	刘彬，张冀聪. 运动相关电位分类算法比较和语义范式分析[J]. 计算机与现代化, 2018, 0(11): 88-.
[6]	马满振，郭理彬，苏奎峰. 基于改进CSP算法的运动想象脑电信号分类方法[J]. 计算机与现代化, 2017, 0(11): 23-28.
[7]	张守中1，肖瑛2. 基于RCE的空间滤波方法在运动想象#br# 电位识别中的应用[J]. 计算机与现代化, 2014, 0(9): 116-119.

基于SSVEP与运动想象的实时脑控阿凡达系统

Real-time Brain-controlled Avatar System Based on SSVEP and Motor Imagery

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价