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DOI:
Journal of Software:2018.29(S2):108-119

脑机接口辅助的动态目标选择技术
孙伟,黄进,李念龙,范向民,田丰,戴国忠,王宏安
(人机交互北京市重点实验室(中国科学院 软件研究所), 北京 100190;中国科学院大学 计算机与控制学院, 北京 100190)
BCI Assisted Dynamic Target Selection Technique
SUN Wei,HUANG Jin,LI Nian-Long,FAN Xiang-Min,TIAN Feng,DAI Guo-Zhong,WANG Hong-An
(Beijing Key Laboratory of Human-Computer Interaction(Institute of Software, The Chinese Academy of Sciences), Beijing 100190, China;School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100190, China)
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Received:June 15, 2018    
> 中文摘要: 动态目标选择是现代交互界面中最为基础的交互任务之一,目前存在多种辅助技术,但这些技术的决策和参数设定有很强的经验性,无法根据用户的当前状态进行调整.为了解决这个问题,基于两个认知负荷与难度感知的假设,提出一种脑机接口辅助的动态目标选择技术,利用近红外光谱信号对用户认知负荷感知的敏感性,实时地调整目标选择技术参数,给不同用户个体提供个性化辅助,适用于不同场景、用户状态和任务难度.通过一组实验,对提出的假设进行了验证,并且基于该假设构建的脑机接口辅助的动态目标选择技术,较不作任何辅助和固定辅助技术两种方案都更优,具体地,在选择错误率上分别降低20.55%和12.09%,在完成时间上分别降低998.35ms和208.67ms.
Abstract:Dynamic target selection is one of the most basic interactive tasks in modern interaction interfaces. There are a variety of assistive techniques, but the design and parameters of these techniques are largely based on experimental data and cannot be adjusted according to the users' current state. In order to solve this problem, a brain-computer interface assisted dynamic target selection technique based on two assumptions of cognitive load and difficulty perception in this study is proposed, which uses the functional near-infrared spectroscopy (fNIRS) signals to cognitive load perception of users and adjusts the parameters of target selection techniques in real time. This technique can provide personalized assistance to different users and be applicable to different scenarios, user status and task difficulty. The proposed hypothesis through a set of experiments is verified, and brain-computer assisted dynamic target selection technique constructed based on this assumption is better than both the auxiliary and fixed auxiliary technologies. Specifically, the selection error rate is reduced by 20.55% and 12.09% respectively, and the completion time is reduced by 998.35 ms and 208.67 ms respectively.
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基金项目:国家重点研发计划(2017YFB1002504);重庆市科技服务平台专项(cstc2015ptfw-ggfw120002) 国家重点研发计划(2017YFB1002504);重庆市科技服务平台专项(cstc2015ptfw-ggfw120002)
Foundation items:National Key Research & Development Plan of China (2017YFB1002504); Science and Technology Service Platform Project of Chongqing Science and Technology Commission (cstc2015ptfw-ggfw120002)
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孙伟,黄进,李念龙,范向民,田丰,戴国忠,王宏安.脑机接口辅助的动态目标选择技术.软件学报,2018,29(S2):108-119

SUN Wei,HUANG Jin,LI Nian-Long,FAN Xiang-Min,TIAN Feng,DAI Guo-Zhong,WANG Hong-An.BCI Assisted Dynamic Target Selection Technique.Journal of Software,2018,29(S2):108-119