SSVEP基础实验步骤与人形机器人脑机器人互动
Summary
The overall goal of this method is to establish an SSVEP-based experimental procedure by integrating multiple software programs to enable the study of brain-robot interaction with humanoid robots, which is prospective in assisting the sick and elderly as well as performing unsanitary or dangerous jobs.
Abstract
Brain-Robot Interaction (BRI), which provides an innovative communication pathway between human and a robotic device via brain signals, is prospective in helping the disabled in their daily lives. The overall goal of our method is to establish an SSVEP-based experimental procedure by integrating multiple software programs, such as OpenViBE, Choregraph, and Central software as well as user developed programs written in C++ and MATLAB, to enable the study of brain-robot interaction with humanoid robots.
This is achieved by first placing EEG electrodes on a human subject to measure the brain responses through an EEG data acquisition system. A user interface is used to elicit SSVEP responses and to display video feedback in the closed-loop control experiments. The second step is to record the EEG signals of first-time subjects, to analyze their SSVEP features offline, and to train the classifier for each subject. Next, the Online Signal Processor and the Robot Controller are configured for the online control of a humanoid robot. As the final step, the subject completes three specific closed-loop control experiments within different environments to evaluate the brain-robot interaction performance.
The advantage of this approach is its reliability and flexibility because it is developed by integrating multiple software programs. The results show that using this approach, the subject is capable of interacting with the humanoid robot via brain signals. This allows the mind-controlled humanoid robot to perform typical tasks that are popular in robotic research and are helpful in assisting the disabled.
Introduction
脑机器人交互（BRI），它提供了人，并通过大脑信号的磁带设备之间的创新的通信通道，是未来的在帮助残疾人在日常生活中1,2。各种各样的方法都能够获得大脑信号要么侵入或非侵入性，如脑电图（脑电图），脑电图（EEG），功能磁共振成像（fMRI）等用于建立最常用的非侵入性的方法BRI系统是获得从置于头皮上的电极脑电信号。这种方法是便宜的，易于使用，并提供了一个可接受的时间分辨率3。在众多的机器人设备，仿人机器人是先进的，因为他们创造了模仿一些人类每天接受同样的身体和精神的任务。 BRI，带仿人机器人将在协助老弱病残，以及执行不卫生或危险的工作具有重要作用。但控制通过BRI系统仿人机器人是极具挑战性，因为仿人机器人全身运动发展到执行复杂的任务，如个人协助4，5。
稳态视觉诱发电位（SSVEP）是在给定频率6诱发的视觉刺激的调制类型的脑信号的。它包含血窦在闪烁刺激的基波和谐波频率，并显着地出现在整个头皮7枕骨区域的视觉皮层。之所以选择SSVEP信号是，SSVEP为基础的BRI系统产生较高的信息传输速率，并需要更少的培训8。其他类型的脑电波，如事件相关电位（ERP的）9或马达成像（MI）的电位10，也可以嵌入到该实验步骤。
我们与仿人脑机器人互动过程机器人是基于Cerebot -一种思维控制类人机器人平台-包括脑电数据采集系统和人形机器人11。脑电图系统是能够记录，预处理和由各种类型的电极获取的显示生物电势信号。它提供了多个模拟的I / O和数字I / O，并且能够同时记录多达128个信号通道以30千赫具有16位分辨率的采样率的。它在C ++和MATLAB软件开发工具包便于用户设计实验步骤。人形机器人具有25个自由度，并配备有多个传感器，包括2个摄像头，麦克风4，2声纳测距仪，2 IR发射器和接收器，1惯性板，9触觉传感器，和8的压力传感器。它提供了Choregraphe和C ++ SDK用于创建和编辑的动作和互动的机器人行为。
这种方法的总体目标是建立一个SSVEP为基础的实验PROCE杜热通过集成多个软件程序，如OpenViBE，Choregraph，中央软件以及写在C ++和MATLAB用户开发的程序，以实现与人形机器人11脑机器人相互作用的研究。 图1显示了系统的结构。专用刺激呈现计算机（SPC）显示用户界面以提供与视觉刺激，指示和环境反馈的主题。专用数据处理计算机（DPC）运行在数据记录器和离线数据分析器中的离线训练的过程中，并运行在线信号处理器和机器人控制器的人形机器人的在线控制。与其他SSVEP为基础的控制系统相比，我们的系统更可靠，更灵活，尤其是更方便的重复使用和更新，因为它是通过将若干标准化的软件包，诸如OpenViBE，Choregraph，中部软件开发的，并模块用C ++编写和MATLAB。
下面的过程进行了审查并批准了天津医科大学总医院伦理委员会，和所有科目签署知情同意书。
Protocol
Representative Results
Discussion
本文提出了一种SSVEP为基础的实验过程通过集成多个软件程序，建立脑机器人交互系统，人形机器人。因为人的意图是通过解释实时EEG信号感知，关键是在进行实验之前验证电极连接和脑电图信号质量。如果从所有的电极获得的信号是差的品质，就必须首先检查接地电极和参考电极的连接。如果有问题，电极的部分，重新注入传导凝胶来调整这些信道的阻抗。
同时获取脑…
Divulgations
The authors have nothing to disclose.
Acknowledgements
作者想表达自己的感激之情洪虎先生在执行本文报道的实验援助。这项工作是由中国国家自然科学基金（61473207号）的部分支持。
Materials
| Cerebus EEG Data Acquisition System | Blackrock Microsystems | 4176-9967 | |
| NAO humanoid robot | Aldebaran Robotics | H25 | |
| EEG cap | Neuroscan | 8732 | |
| Ten20 Conductive gel | Weaver and company | 10-20-8 |
References
- McFarland, D. J., Wolpaw, J. R. Brain-Computer Interface Operation of Robotic and Prosthetic Devices. Computer. 41, 52-56 (2008).
- Lebedev, M. A., Nicolelis, M. A. Brain-machine interfaces: Past, present and future. Trends Neruosci. 29 (9), 536-546 (2006).
- Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., Vaughan, T. M. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767-791 (2002).
- Bell, C. J., Shenoy, P., Chalodhorn, R., Rao, R. P. Control of a humanoid robot by a noninvasive brain-computer interface in humans. J. Neural. Eng. 5, 214-220 (2008).
- Li, W., Li, M., Zhao, J. Control of humanoid robot via motion-onset visual evoked potentials. Front. Syst. Neurosci. 8, 247 (2014).
- Regan, D. Some characteristics of average steady-state and transient responses evoked by modulated light. Electroencephalogr. Clin. Neurophysiol. 20, 238-248 (1966).
- Vialatte, F. B., Maurice, M., Dauwels, J., Cichocki, A. Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. Prog. Neurobiol. 90, 418-438 (2010).
- Bin, G., Gao, X., Wang, Y., Li, Y., Hong, B., Gao, S. A high-speed BCI based on code modulation VEP. J. Neural. Eng. 8, 025015 (2011).
- Sutton, S., Braren, M., Zubin, J., John, E. R. Evoked-potential correlates of stimulus uncertainty. Science. 150, 1187-1188 (1965).
- Pfurtscheller, G., Lopes da Silva, H. F. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842-1857 (1999).
- Zhao, J., Meng, Q., Li, W., Li, M., Sun, F., Chen, G. OpenViBE-based brainwave control system for Cerebot. Proc. IEEE International Conference on Robotics and Biomimetics. , 1169-1174 (2013).
- Homan, R. W., Herman, J., Purdy, P. Cerebral location of international 10-20 system electrode placement. Electroencephalogr. Clin. Neurophysiol. 66, 376-382 (1987).
- Bin, G., Gao, X., Yan, Z., Hong, B., Gao, S. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method. J. Neural. Eng. 6, 046002 (2009).
- Wang, Y., Wang, R., Gao, X., Hong, B., Gao, S. A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 14, 234-239 (2006).
- Chae, Y., Jeong, J., Jo, S. Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI. IEEE T. Robot. 28, 1131-1144 (2012).
- Croft, R. J., Barry, R. J. Removal of ocular artifact from the EEG: a review. Neurophysiol. Clin. 30, 5-19 (2000).
- Hwang, H. J., Hwan Kim, D., Han, C. H., Im, C. H. A new dual-frequency stimulation method to increase the number of visual stimuli for multi-class SSVEP-based brain-computer interface (BCI). Brain Res. 1515, 66-77 (2013).
- Wang, M., Daly, I., Allison, B., Jin, J., Zhang, Y., Chen, L., Wang, X. A new hybrid BCI paradigm based on P300 and SSVEP. J Neurosci Methods. 244, 16-25 (2015).
- Allison, B. Z., Jin, J., Zhang, Y., Wang, X. A four-choice hybrid P300 SSVEP BCI for improved accuracy. Brain-Computer Interfaces. 1, 17-26 (2014).
- Pan, J. H., Xie, Q., Herman, Y., Wang, F., Di, H., Laureys, S., Yu, R., Li, Y. Detecting awareness in patients with disorders of consciousness using a hybrid brain-computer interface. J. Neural. Eng. 11, 056007 (2014).
- Li, J., Ji, H., Cao, L., Zhang, D., Gu, R., Xia, B., Wu, Q. Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multi-degree of freedom. Int J Neural Syst. 24, 1450014-14 (2014).
- Zhao, J., Meng, Q., Li, W., Li, M., Chen, G. SSVEP-based hierarchical architecture for control of a humanoid robot with mind. Proc. 11th World Congress on Intelligent Control and Automation. , 2401-2406 (2014).
- Zhu, D., Bieger, J., Molina, G. G., Aarts, R. M. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci. 2010, 1-12 (2010).
- Muller-Putz, G. R., Scherer, R., Brauneis, C., Pfurtscheller, G. Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng. 2, 123-130 (2005).
