mlsanityData to 14 Oct 2026

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

arXiv 2212.08122 ↗First posted 14 Dec 2022cs.HC cs.AI cs.ROcited by 0

On the map: Knowledge distillation → Distillation for medical imaging

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Distillation for medical imaging, one of 12 directions in Knowledge distillation. Builds on 4 of its 20 references that are in the map. First posted on arXiv 14 Dec 2022.

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4 of 20 references.

References · 4

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2018Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labelscited by 997
  3. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  4. 2020Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural networkcited by 7

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  2. 2024Scheduled Knowledge Acquisition on Lightweight Vector Symbolic Architectures for Brain-Computer Interfaces
  3. 2025Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces
  4. 2025Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
  5. 2021Distilling EEG Representations via Capsules for Affective Computing
  6. 2026BrainDistill: Implantable Motor Decoding with Task-Specific Knowledge Distillation
  7. 2026STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding
  8. 2026AutoBCI: Forecast-Guided Agentic Neural Architecture Discovery for EEG-Based Brain--Computer Interfaces