  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.
