Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control
This work addresses the challenge of practical robotic arm control for users with disabilities, but it is incremental as it builds on existing knowledge distillation techniques.
The authors tackled the problem of controlling a robotic arm using brain-computer interfaces by proposing a knowledge distillation-based framework that uses hybrid paradigm-induced EEG signals, achieving the best performance among singular architecture-based methods.
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.