Objective. Brain–computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC. Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials. Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error. Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

中文翻译：

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在物理人机协作中不同心理工作负载条件下，预测机器人意外行为期间的认知冲突

客观的。脑机接口（BCI）技术有望在现代工作环境中发挥重要作用，尤其是在人类和机器近距离工作（通常是身体接触）的协作环境中。在物理人机协作 (pHRC) 中，机器人执行复杂的运动序列。任何意外的机器人行为或错误的交互都可能引发安全问题。当人类伙伴感知到错误时，大脑自然产生的错误相关电位已被广泛应用于 BCI 中，作为隐式人类反馈，以适应机器人行为，以促进安全和直观的交互。然而，BCI 技术与机器人控制中潜在的错误相关的集成需要高度不确定的脑电图 (EEG) 信号的无故障集成，特别是受用户的身体和认知状态的影响。由于用户的较高工作负荷会影响他们对错误意识所需的认知资源的访问，因此研究心理工作负荷变化如何影响错误意识至关重要，因为这可能会引起 pHRC 的安全问题。在本研究中，我们旨在研究认知工作量如何影响参与 pHRC 的人类用户的错误意识。方法。我们用磨料工业机器人设计了一个喷砂任务，并通过不同难度的辅助算术任务来控制脑力负荷。记录了 24 名移动机器人手臂的参与者的脑电图数据、感知的工作量、任务和身体表现。在 33% 的试验中，错误情况是由于机器人意外停止而导致的。主要成果。我们观察到预测误差负性（PEN）和误差正性（Pe）的幅度减小，表明随着脑力负荷的增加，错误意识降低。我们进一步观察到，与机器人在目标处正确停止时相比，机器人意外停止后，额叶 theta 功率增加，中央 alpha 和中央 beta 功率呈增加趋势。我们还证明了一种流行的卷积神经网络模型 EEGNet 可以根据误差之前的 EEG 数据预测 PEN 和 Pe 的幅度。意义。该预测模型有助于开发在线预测模型，该模型可以预先警告系统和操作员用户错误意识的减弱，暗示 pHRC 的基于错误相关电位的 BCI 系统存在潜在的安全漏洞。因此，我们的工作为在 pHRC 中采用 BCI 技术铺平了道路，以利用实时大脑活动优化机器人行为，实现个性化用户体验，丰富交互质量。