论文标题
用于机器人手臂控制的基于混合范式的大脑计算机接口
Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control
论文作者
论文摘要
脑部计算机界面（BCI）使用脑信号与外部设备进行通信，而无需实际控制。特别是，BCI是控制机器人臂的接口之一。在这项研究中，我们提出了一个基于知识蒸馏的框架，以通过混合范式诱导的EEG信号来操纵机器人臂，以供实际使用。教师模型旨在以层次结构分析输入数据，并将知识转移到学生模型。为此，软标签和蒸馏损失功能应用于学生模型培训。根据实验结果，学生模型在基于单数体系结构的方法中取得了最佳性能。可以证实，使用层次模型和知识蒸馏，可以改善简单体系结构的性能。由于不确定哪些知识要转移，因此在未来的研究中澄清这一部分很重要。
Brain-computer interface (BCI) uses brain signals to communicate with external devices without actual control. Particularly, BCI is one of the interfaces for controlling the robotic arm. In this study, we propose a knowledge distillation-based framework to manipulate robotic arm through hybrid paradigm induced EEG signals for practical use. The teacher model is designed to decode input data hierarchically and transfer knowledge to student model. To this end, soft labels and distillation loss functions are applied to the student model training. According to experimental results, student model achieved the best performance among the singular architecture-based methods. It is confirmed that using hierarchical models and knowledge distillation, the performance of a simple architecture can be improved. Since it is uncertain what knowledge is transferred, it is important to clarify this part in future studies.
