ハイブリッドパラダイムBCIによるロボットアーム制御
Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control
脳波信号からロボットアームを操作するため、階層的な教師モデルから知識蒸留で学生モデルを訓練する枠組みを提案し、単純なアーキテクチャでも性能が向上することを示した。
著者: Byeong-Hoo Lee, Jeong-Hyun Cho, Byung-Hee Kwon
分類: cs.HC, cs.AI, cs.RO, cs.SY, eess.SY
原文アブストラクト
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.