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

TL;DR

This paper introduces a hybrid paradigm-based brain-computer interface framework utilizing knowledge distillation to enhance robotic arm control via EEG signals, demonstrating improved performance over single-architecture methods.

Contribution

It presents a novel knowledge distillation framework with hierarchical models for BCI-based robotic arm control, improving simple model performance.

Findings

Student model outperforms single-architecture methods

Hierarchical models and knowledge distillation enhance BCI performance

Performance improvement confirmed through experimental results

Abstract

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.