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Received:February 23, 2019 Revised:March 14, 2019
Received:February 23, 2019 Revised:March 14, 2019
中文摘要: 传统的脑机接口（Brain Computer Interface，BCI）有许多不足，如基于运动想象的BCI需要受试者进行大量练习；基于P300位的BCI需要多次重复闪烁；基于SSVEP的BCI上的控制命令数量受刺激频率及其他因素影响.为此，研究人员提出了混合脑机接口（hybrid Brian Computer Interface，hBCI）.本文主要讨论了hBCI的研究进展，综述了常见的三种hBCI类型，分别是基于多种大脑模式的hBCI、基于多种感官刺激的hBCI、基于多种信号的hBCI，通过分析最新的hBCI系统的一般原理、刺激范式、实验结果、优点和应用，发现利用hBCI技术可以提高BCI的分类准确率，增加控制命令的数量，明显优于单一模态的BCI.
Abstract:Traditional Brain-Computer Interface (BCI) systems have many shortcomings, such as BCI based on motor imaginary requires a large number of exercises; BCI based on P300 requires multiple repetitions of flicker; the number of control commands of BCI based on SSVEP is affected by stimulation frequency and other factors. To this end, researchers have proposed a hybrid Brian-Computer Interface (hBCI). This paper mainly discusses the research progress of hBCI, and reviews three common types of hBCI, such as hBCI of multiple brain models, hBCI of various sensory stimuli, and hBCI of various signals. By analyzing the general principles, stimulation paradigms, experimental results, advantages and applications of the latest hBCI system, we find that using hBCI technology can improve the classification accuracy of BCI and increase the number of control commands, which is obviously better than single mode BCI.
keywords: hybrid Brain-Computer Interfaces (hBCI) P300 potential SSVEP control commands classification accuracy
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基金项目:国家自然科学基金（61876067）；国家自然科学基金青年科学基金（61503143）；广州市科技计划项目珠江科技新星科技创新人才专项（201710010038）
引用文本：
李自娜,徐欢,潘家辉.混合脑机接口的研究进展.计算机系统应用,2019,28(9):1-8
LI Zi-Na,XU Huan,PAN Jia-Hui.Advances in Hybrid Brain-Computer Interfaces.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):1-8
李自娜,徐欢,潘家辉.混合脑机接口的研究进展.计算机系统应用,2019,28(9):1-8
LI Zi-Na,XU Huan,PAN Jia-Hui.Advances in Hybrid Brain-Computer Interfaces.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):1-8
