Appropriate robot behavior during human-robot interaction is a key part in the development of human-compliant assistive robotic systems. This study poses the question of how to continuously evaluate the quality of robotic behavior in a hybrid brain-computer interfacing (BCI) task, combining brain and non-brain signals, and how to use the collected information to adapt the robot's behavior accordingly. To this aim, we developed a rating system compatible with EEG recordings, requiring the users to execute only small movements with their thumb on a wireless controller to rate the robot's behavior on a continuous scale. The ratings were recorded together with dry EEG, respiration, ECG, and robotic joint angles in ROS. Pilot experiments were conducted with three users that had different levels of previous experience with robots. The results demonstrate the feasibility to obtain continuous rating data that give insight into the subjective user perception during direct human-robot interaction. The rating data suggests differences in subjective perception for users with no, moderate, or substantial previous robot experience. Furthermore, a variety of regression techniques, including deep CNNs, allowed us to predict the subjective ratings. Performance was better when using the position of the robotic hand than when using EEG, ECG, or respiration. A consistent advantage of features expected to be related to a motor bias could not be found. Across-user predictions showed that the models most likely learned a combination of general and individual features across-users. A transfer of pre-trained regressor to a new user was especially accurate in users with more experience. For future research, studies with more participants will be needed to evaluate the methodology for its use in practice. Data and code to reproduce this study are available at https://github.com/TNTLFreiburg/NiceBot.

中文翻译：

---

符合人类要求的机器人的混合脑计算机接口：利用深度回归推断连续的主观评分。

人机交互过程中适当的机器人行为是开发符合人类标准的辅助机器人系统的关键部分。这项研究提出了一个问题，即如何在混合的脑机接口（BCI）任务中结合大脑和非大脑信号来不断评估机器人行为的质量，以及如何使用收集到的信息来相应地适应机器人的行为。为此，我们开发了一种与EEG记录兼容的评估系统，要求用户用拇指在无线控制器上仅执行小动作即可连续评估机器人的行为。记录的评分以及ROS中的干性EEG，呼吸，ECG和机器人关节角度。这项实验是对三个以前在机器人方面有不同经验的用户进行的。结果表明，获得连续的评分数据的可行性是可行的，这些数据可以在人机直接交互过程中洞察主观用户的感知。评级数据表明，以前没有，没有中等经验或没有大量机器人经验的用户在主观感知上存在差异。此外，包括深度CNN在内的多种回归技术使我们能够预测主观评分。使用机械手的位置时的性能要优于使用EEG，ECG或呼吸时的性能。找不到与电动机偏置有关的功能的一致优势。跨用户的预测表明，这些模型最有可能在跨用户的基础上学习了一般功能和个人功能的组合。在有更多经验的用户中，将经过预训练的回归器转移到新用户中尤其准确。对于将来的研究，将需要有更多参与者的研究来评估该方法在实践中的使用。复制此研究的数据和代码可在https://github.com/TNTLFreiburg/NiceBot获得。