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Brain-computer interface (BCI) uses brain signals to communicate withexternal devices without actual control. Particularly, BCI is one of theinterfaces for controlling the robotic arm. In this study, we propose aknowledge distillation-based framework to manipulate robotic arm through hybridparadigm induced EEG signals for practical use. The teacher model is designedto decode input data hierarchically and transfer knowledge to student model. Tothis end, soft labels and distillation loss functions are applied to thestudent model training. According to experimental results, student modelachieved the best performance among the singular architecture-based methods. Itis confirmed that using hierarchical models and knowledge distillation, theperformance of a simple architecture can be improved. Since it is uncertainwhat knowledge is transferred, it is important to clarify this part in futurestudies.

Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control