2 citations · 4 across the 12 of their papers we have counts for
12 papers
Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models
Myeong-Ju Cho, Hye-Bin Shin, Seo-Hyun Lee +1
Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments,…
Decoding Error-Related Potentials under Multisensory Feedback with Varying Congruency
Yixin Liu, Kang Yin, Hye-Bin Shin +1
Error-related potentials (ErrPs) are widely studied neural signatures associated with error processing in human-machine interaction. In realistic settings, error perception often o…
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…
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge
Dan Li, Hye-Bin Shin, Kang Yin +1
The significant inter-subject variability in electroen-cephalogram (EEG) signals often results in substantial changes to neural network weights as data distributions shift. This va…
EEG-based Multimodal Representation Learning for Emotion Recognition
Kang Yin, Hye-Bin Shin, Dan Li +1
Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availab…
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution
Hye-Bin Shin, Kang Yin, Seong-Whan Lee
In the quest for efficient neural network models for neural data interpretation and user intent classification in brain-computer interfaces (BCIs), learning meaningful sparse repre…
Pioneering EEG Motor Imagery Classification Through Counterfactual Analysis
Kang Yin, Hye-Bin Shin, Hee-Dong Kim +1
The application of counterfactual explanation (CE) techniques in the realm of electroencephalography (EEG) classification has been relatively infrequent in contemporary research. I…