2 citations · 4 across the 12 of their papers we have counts for
5 papers · 1 filter
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,…
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…
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…
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…
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…