非侵入式脑—机接口编解码技术研究进展  

A survey on encoding and decoding technology of non-invasive brain-computer interface

作　　者：邱爽 杨帮华[3] 陈小刚[4] 王毅军[5] 许敏鹏[6] 吕宝粮 高小榕[8] 何晖光[1,2] Qiu Shuang;Yang Banghua;Chen Xiaogang;Wang Yijun;Xu Minpeng;Lyu Baoliang;Gao Xiaorong;He Huiguang(State Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing 100049,China;School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China;Institute of Biomedical Engineering,Chinese Academy of Medical Sciences,Tianjin 300192,China;Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China;School of Precision Instrument and Opto-Electronics Engineering,Academy of Medical Engineering and Translational Medicine,Tianjin University,Tianjin 300072,China;School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;School of Medicine,Tsinghua University,Beijing 100084,China)

机构地区：[1]中国科学院自动化研究所多模态人工智能系统实验室,北京100190 [2]中国科学院大学人工智能学院,北京100049 [3]上海大学机电工程与自动化学院,上海200444 [4]中国医学科学院生物医学工程研究所,天津300192 [5]中国科学院半导体研究所,北京100083 [6]天津大学精密仪器与光电子工程学院,医学工程与转化医学研究院,天津300072 [7]上海交通大学电子信息与电气工程学院,上海200240 [8]清华大学医学院,北京100084

出　　处：《中国图象图形学报》2023年第6期1543-1566,共24页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62020106015,U21A20388);中国科学院B类战略性先导科技专项项目(XDB32040200)。

摘　　要：脑—机接口(brain-computer interface,BCI)系统通过采集、分析大脑信号,将其转换为输出指令,从而跨越外周神经系统,实现由大脑信号对外部设备的直接控制,进而用于替代、修复、增强、补充或改善中枢神经系统的正常输出。非侵入式脑—机接口由于具有安全性以及便携性等优点,得到了广泛关注和持续研究。研究人员对脑信号编码方法的不断探索扩展了BCI系统的应用场景和适用范围。同时,脑信号解码方法的不断研发极大地克服了脑电信号信噪比低的缺点,提高了系统性能,这都为构建高性能脑—机接口系统奠定了基础。本文综述了非侵入式脑—机接口编解码技术以及系统应用的最新研究进展,展望其未来发展前景,以期促进BCI系统的深入研究与广泛应用。A brain-computer interface(BCI)establishes a direct communication pathway between the living brain and an external device in terms of brain signals-acquiring and analysis and commands-converted output.It can be used to replace,repair,augment,supplement or improve the normal output of the central nervous system.The BCIs can be divided into invasive or noninvasive BCIs based on the placement of the acquisition electrode.An invasive BCI is linked to its records or brain neurons-relevant stimulation through surgical brain-implanted electrodes.But,it is just used for animal experiments or severe paralysis patients although invasive BCIs can be used to record a high signal-to-noise ratio-related brain signals.Non-invasive BCIs have its potentials of their credibility and portability in comparison with invasive BCIs.The electroencephalography(EEG)is commonly used for brain signal for BCIs now.The EEG-based BCI systems consist of two directions:coding methods used to generate brain signals and the decoding methods used to decode brain signals.In recent years,the growth of coding methods has extended the application scenarios and applicability of the system.Furthermore,to get high-performance BCIs,brain signal decoding methods has greatly developed and low signal-to-noise ratio of EEG signals is optimized as well.The BCI systems can be segmented into such categories of active,reactive or passive.For an active BCI,a user can consciously control mental activities of external stimuli excluded.The motor imagery based(MIbased)BCI system is an active BCI system in terms of EEG signals-within specific frequency changes,which can be balanced using non-motor output mental rehearsal of a motor action.For reactive BCI,brain activity is triggered by an external stimulus,and the user reacts to the stimulus from the external world.Most researches are focused on static-state visual evoked potential based(SSVEP-based)BCI and an event-related potential based(ERP-based)BCI.SSVEPs are brain responses that are elicited over the visual region

关 键 词：脑—机接口(BCI) 非侵入 编码方法 解码方法 系统应用 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]