基于SSVEP＿SSA融合的混合脑机接口研究  

Research on hybrid BCI system combined SSVEP and SSA

作　　者：韩向可 郭士杰[1,3] Han Xiangke;Guo Shijie(School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China;School of Mechanical Engineering, Anyang Institute of Technology, Anyang 455000, China;Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin 300130, China)

机构地区：[1]河北工业大学机械工程学院,天津300130 [2]安阳工学院机械工程学院,安阳455000 [3]河北省机器人感知与人机融合重点实验室,天津300130

出　　处：《仪器仪表学报》2019年第5期213-220,共8页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(61871173)项目资助

摘　　要：针对目前基于体感选择性注意范式的脑机接口控制指令数少,信息传输率低等缺点,提出了一种全新的多模态混合脑机接口系统。该系统融合稳态视觉刺激(SSVEP)和体感选择性注意范式(SSA),在外部视觉和体感刺激的作用下,诱发大脑产生稳态视觉电位和事件相关去同步现象。同时,为了解决传统脑电信息特征提取中需要大量先验知识等问题,引入深度学习算法对混合脑机接口信息进行意图解码,该方法将多通道的时域信息转换成具有时-频-空域三维特征的二维特征图。对8名受试者的离线实验显示,平均识别准确率达到81.35%,确认了所提出的基于SSVEP_SSA融合的多模态混合脑机接口是可行的,实现了脑机接口(BCI)系统的指令集扩展和高精度解码。The current somatosensory selective attention based brain computer in terface ( BCI) system has disadvantage of less command for multi-degree deviceandlow information transmission rate. To solve these problems, a novel hybrid BCI system combing steady-state visual evoked potential ( SSVEP) and somatosensory selective attention ( SSA) is proposed in the paper. The SSVEP and event related desynchronization ( ERD) can be elicited withtheaidof visual and somatosensory stimuli. In order to overcome the shortcomings of conventional feature extraction method which needs more heuristic knowledge, a deep learning algorithm is used to decode the EEG signal. In this method, the temporal-domainsignals of several channels are converted into temporal-frequency-spatial domain feature image. Eight subjects arerecruited to participate the experiment. The average accuracy of offline test is 81.35%, which indicates that the proposed multi-modal hybrid BCI based on SSVEP_SSA is feasible for instruction set extension and decoding precisely.

关 键 词：多模态 混合脑机接口 体感选择性注意 卷积神经网络 

分 类 号：R318[医药卫生—生物医学工程] TH77[医药卫生—基础医学]