On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics
On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics
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DOI：
10.1371/journal.pone.0081732
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发表时间：
2013-12-16
期刊：
影响因子：
3.7
通讯作者：
Fahle, Manfred
中科院分区：
文献类型：
--
作者：
Kirchner, Elsa Andrea;Kim, Su Kyoung;Fahle, Manfred
今天的机器人在日常活动中自主支持人类的能力仍然有限。为了改善这一点，可以应用预测人机界面（HMI）来更好地支持人机之间的未来交互。为了推断即将到来的基于上下文的行为，必须检测人类的相关大脑状态。这是通过大脑阅读（BR）来实现的，BR是一种使用监督机器学习（ML）方法进行单次试验EEG分析的被动方法。在这项工作中，我们提出，BR是能够检测到具体的状态的相互作用的人。为了支持这一点，我们表明，BR检测模式的脑电图（EEG），可以与事件相关的活动在EEG中，如P300，这是具体的状态或大脑过程的指标，如目标识别过程。此外，我们通过识别和结合最相关的训练数据进行单次试验分类和应用分类器迁移，提高了BR在面向应用场景中的鲁棒性和适用性。我们表明，训练和测试，即，如果两个类的样本都错过了相关的模式，则分类器的应用可以在不同的类上执行。分类器迁移对于BR在只有少量训练样本可用的应用场景中的使用非常重要。最后，我们展示了一个双BR应用程序中的实验设置，需要类似的行为，在机器人手臂的遥操作。在这里，目标识别过程和运动准备过程中同时检测。总之，我们的研究结果有助于开发强大和稳定的预测HMI，使不同的交互行为的同时支持。
The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.