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异步BCI的大脑工作和空闲状态的辨识方法研究
引用本文:宫铭鸿,徐桂芝,李梦凡,林 放. 异步BCI的大脑工作和空闲状态的辨识方法研究[J]. 电子测量与仪器学报, 2020, 34(4): 11-19
作者姓名:宫铭鸿  徐桂芝  李梦凡  林 放
作者单位:1.河北工业大学省部共建电工装备可靠性与智能化国家重点实验室,2.河北工业大学天津市生物电工与智能健康重点实验室
基金项目:河北省自然科学基金(F2018202088)、国家自然科学基金(61806070,51737003,51977060)资助项目
摘    要:基于事件相关电位的脑-机接口系统难以检测大脑的空闲状态,限制了被试在任意时间输出指令的自由。利用欧德堡范式同时诱发N200电位、P300电位和瞬态视觉诱发电位。根据瞬态视觉诱发电位的频域特征区分大脑的工作状态和空闲状态;在工作状态下利用N200和P300电位的时域特征识别被试的控制意图,从而构建异步的脑-机接口系统。通过对7名健康被试进行发送指令与观看视频反馈两种状态的实验,实现大脑的工作状态和空闲状态之间的切换。该方法识别大脑状态或者意图的准确率为98.21%,与基于事件相关电位识别空闲状态的方法相比提高了50.89%。

关 键 词:脑-机接口  事件相关电位  瞬态视觉诱发电位  异步系统

Research on identification method of brain working and idle state of asynchronous BCI
Gong Minghong,Xu Guizhi,Li Mengfan,Lin Fang. Research on identification method of brain working and idle state of asynchronous BCI[J]. Journal of Electronic Measurement and Instrument, 2020, 34(4): 11-19
Authors:Gong Minghong  Xu Guizhi  Li Mengfan  Lin Fang
Affiliation:1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology,2. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health
Abstract:The brain computer interface system based on event related potentials is difficult to detect the idle state of the brain, which limits the freedom of the subjects to output instructions at any time. Oddball paradigm can induce the N200 potential, P300 potential and transient visual evoked potential simultaneously. the working state and idle state of the brain are distinguished according to the frequency domain characteristics of transient visual evoked potential. In the working state, the time domain features of N200 and P300 potential are used to identify the control intention of the subject. Through the experiment of sending instructions and watching video feedback on seven healthy subjects, the switch between the working state and the idle state of the brain is realized. The accuracy of the method to identify the state or intention of the brain is 98.21%, which is 50.89% higher than that of the method to identify the idle state based on the event related potential.
Keywords:brain-computer interface   event related potential   transient visual evoked potential   asynchronous system
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