2 citations · 4 across the 12 of their papers we have counts for
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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…
NeuroLex: A Lightweight Domain Language Model for EEG Report Understanding and Generation
Kang Yin, Hye-Bin Shin
Clinical electroencephalogram (EEG) reports encode domain-specific linguistic conventions that general-purpose language models (LMs) fail to capture. We introduce NeuroLex, a light…
Toward Adaptive BCIs: Enhancing Decoding Stability via User State-Aware EEG Filtering
Yeon-Woo Choi, Hye-Bin Shin, Dan Li
Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user attention fluctuates, brain stat…
Prototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding
Dan Li, Hye-Bin Shin, Yeon-Woo Choi
Due to the significant variability in electroencephalo-gram (EEG) signals across individuals, knowledge acquired from previous subjects is often overwritten as new subjects are int…
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Suzie Kim, Hye-Bin Shin, Seong-Whan Lee
Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-spe…
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…