• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Frontiers in Human Neuroscience >An Intelligent Man-Machine Interface—Multi-Robot Control Adapted for Task Engagement Based on Single-Trial Detectability of P300
【2h】

An Intelligent Man-Machine Interface—Multi-Robot Control Adapted for Task Engagement Based on Single-Trial Detectability of P300

机译：智能人机界面-基于P300的单次可检测性适用于任务参与的多机器人控制

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Advanced man-machine interfaces (MMIs) are being developed for teleoperating robots at remote and hardly accessible places. Such MMIs make use of a virtual environment and can therefore make the operator immerse him-/herself into the environment of the robot. In this paper, we present our developed MMI for multi-robot control. Our MMI can adapt to changes in task load and task engagement online. Applying our approach of embedded Brain Reading we improve user support and efficiency of interaction. The level of task engagement was inferred from the single-trial detectability of P300-related brain activity that was naturally evoked during interaction. With our approach no secondary task is needed to measure task load. It is based on research results on the single-stimulus paradigm, distribution of brain resources and its effect on the P300 event-related component. It further considers effects of the modulation caused by a delayed reaction time on the P300 component evoked by complex responses to task-relevant messages. We prove our concept using single-trial based machine learning analysis, analysis of averaged event-related potentials and behavioral analysis. As main results we show (1) a significant improvement of runtime needed to perform the interaction tasks compared to a setting in which all subjects could easily perform the tasks. We show that (2) the single-trial detectability of the event-related potential P300 can be used to measure the changes in task load and task engagement during complex interaction while also being sensitive to the level of experience of the operator and (3) can be used to adapt the MMI individually to the different needs of users without increasing total workload. Our online adaptation of the proposed MMI is based on a continuous supervision of the operator's cognitive resources by means of embedded Brain Reading. Operators with different qualifications or capabilities receive only as many tasks as they can perform to avoid mental overload as well as mental underload.
机译：正在开发先进的人机界面（MMI），以在遥远且难以接近的地方遥控机器人。这种MMI利用虚拟环境，因此可以使操作员将自己沉浸在机器人的环境中。在本文中，我们介绍了我们为多机器人控制开发的MMI。我们的MMI可以适应在线任务负载和任务参与的变化。应用我们的嵌入式Brain Reading方法，我们可以提高用户支持和交互效率。从互动过程中自然诱发的与P300相关的大脑活动的单次试验可知性，可以推断出任务投入的程度。使用我们的方法，不需要第二项任务来衡量任务负载。它基于对单刺激范例，脑资源分布及其对P300事件相关组件的影响的研究结果。它进一步考虑了由于对与任务相关的消息的复杂响应而引起的对P300组件的延迟反应时间所引起的调制效果。我们使用基于单次尝试的机器学习分析，平均事件相关电位的分析和行为分析来证明我们的概念。作为主要结果，我们证明了（1）与所有受试者都可以轻松执行任务的设置相比，执行交互任务所需的运行时间有了显着改善。我们证明（2）事件相关电位P300的单次可检测性可用于测量复杂交互过程中任务负载和任务参与的变化，同时还对操作员的经验水平敏感；（3）可用于使MMI单独适应用户的不同需求，而不会增加总工作量。我们对建议的MMI的在线改编是基于通过嵌入式大脑阅读对操作员认知资源的持续监督。具有不同资历或能力的操作员只能接收尽可能多的任务，以避免精神上的超负荷和精神上的欠载。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号