4 papers
Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces
Hyo-Jeong Jang, Hye-Bin Shin, Seong-Whan Lee
Electroencephalography (EEG) is a fundamental modality for cognitive state monitoring in brain-computer interfaces (BCIs). However, it is highly susceptible to intrinsic signal err…
Accelerating Reinforcement Learning via Error-Related Human Brain Signals
Suzie Kim, Hye-Bin Shin, Hyo-Jeong Jang
In this work, we investigate how implicit neural feed back can accelerate reinforcement learning in complex robotic manipulation settings. While prior electroencephalogram (EEG) gu…
Cross-Modal Consistency-Guided Active Learning for Affective BCI Systems
Hyo-Jeong Jang, Hye-Bin Shin, Kang Yin
Deep learning models perform best with abundant, high-quality labels, yet such conditions are rarely achievable in EEG-based emotion recognition. Electroencephalogram (EEG) signals…
Uncertainty-Resilient Multimodal Learning via Consistency-Guided Cross-Modal Transfer
Hyo-Jeong Jang
Multimodal learning systems often face substantial uncertainty due to noisy data, low-quality labels, and heterogeneous modality characteristics. These issues become especially cri…