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作 者:谢平[1,2] 门延帝 甄嘉乐 邵谢宁 赵靖 陈晓玲 XIE Ping;MEN Yandi;ZHEN Jiale;SHAO Xiening;ZHAO Jing;CHEN Xiaoling(College of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066000,P.R.China;Key Lab of Intelligent Rehabilitation and Neuromodulation of Hebei Province,Qinhuangdao,Hebei 066000,P.R.China)
机构地区:[1]燕山大学电气工程学院,河北秦皇岛066000 [2]河北省智能康复与神经调控重点实验室,河北秦皇岛066000
出 处:《生物医学工程学杂志》2024年第4期664-672,共9页Journal of Biomedical Engineering
基 金:国家自然科学基金(U20A20192);河北省自然科学基金(F2022203079,F2022203002);河北省创新能力提升计划项目(22567619H)。
摘 要:基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)在智能机器人领域的应用备受关注。传统基于SSVEP的BCI系统多采用同步触发方式,没有识别用户是处于控制态还是非控制态,导致系统缺乏自主控制能力。为此,本文提出了一种SSVEP异步状态识别方法,通过融合脑电信号(EEG)的多种时频域特征,结合线性判别分类器构建了异步状态识别模型,提高SSVEP异步状态识别准确率。进一步,针对残障人群在多任务场景下的控制需求,搭建了一种基于SSVEP-BCI异步协同控制的脑机融合系统,实现在复杂场景下可穿戴机械手与机械臂即“第三只手”的协同控制。实验结果表明,运用本文所提出的SSVEP异步控制算法和脑机融合系统,可以辅助用户完成多任务协同操作,在线控制实验中用户意图识别的平均准确率为93.0%,为SSVEP异步脑机接口系统的实际应用提供了理论和实践依据。Brain-computer interface(BCI)based on steady-state visual evoked potential(SSVEP)have attracted much attention in the field of intelligent robotics.Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state,resulting in a system that lacks autonomous control capability.Therefore,this paper proposed a SSVEP asynchronous state recognition method,which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic(EEG)signals and combining with a linear discriminant analysis(LDA)to improve the accuracy of SSVEP asynchronous state recognition.Furthermore,addressing the control needs of disabled individuals in multitasking scenarios,a brainmachine fusion system based on SSVEP-BCI asynchronous cooperative control was developed.This system enabled the collaborative control of wearable manipulator and robotic arm,where the robotic arm acts as a“third hand”,offering significant advantages in complex environments.The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations.The average accuracy of user intent recognition in online control experiments was 93.0%,which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.
关 键 词:异步脑机接口 稳态视觉诱发电位 外肢体机械人 增强现实
分 类 号:R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]
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