Hyunwook Kang
Hi, I'm a PhD student in medical AI.
My primary research interests include multimodal learning, explainable AI, Riemannian manifolds,
and effects of brain connectivity in emotional dynamics and imagined speech.
Publications
International Conferences
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Multimodal Emotion Recognition from EEG and ECG Signals via Parallel Fusion of Graph Convolution and LSTMs
Hyunwook Kang, Minji Lee
EMBC-the 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2025- Phoneme Classification in Imagined Speech Using Explainable Machine Learning
Sejin Kim, Hyunwook Kang, Ji-Hoon Jeong, Minji Lee
WBCI-the 13th International Winter Conference on Brain-Computer Interface, 2025- Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition
Hyunwook Kang, Jin Woo Choi, Byung Hyung Kim
ICPR-International Conference on Pattern Recognition, 2024- Zero-shot visual emotion recognition by exploiting BERT
Hyunwook Kang, Devamanyu Hazarika, Dongho Kim, Jihie Kim
IntelliSys-Intelligent Systems Conference, 2022International Journals
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Convolutional Channel Modulator for Transformer and LSTM networks in EEG-based emotion recognition
Hyunwook Kang, Jin Woo Choi, Byung Hyung Kim
Biomed. Eng. Lett.-Biomedical Engineering Letters (2023-JCR IF: 3.2), 2025Domestic Journals
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ConTL: Improving the Performance of EEG-based Emotion Recognition via the
Incorporation of CNN, Transformer and LSTM
Hyunwook Kang, Byung Hyung Kim
JOK-Journal of Korean Institute of Information Scientists and Engineers, 2024- Efficient Visual Sentiment Detector using Knowledge Distillation
Hyun Wook Kang, Kwangil Kim
JKIIT-Journal of Korean Institute of Information Technology, 2021Awards
Outstanding paper award from Inha University in 2025.Education
The Catholic University of Korea
Bucheon, South Korea
Degree: PhD of Healthcare and Artificial Intelligence
Incheon, South Korea
Degree: Master of Electrical and Computer Engineering
Callaghan, Australia
Degree: Bachelor of Information Technology
- Efficient Visual Sentiment Detector using Knowledge Distillation
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ConTL: Improving the Performance of EEG-based Emotion Recognition via the
Incorporation of CNN, Transformer and LSTM
- Phoneme Classification in Imagined Speech Using Explainable Machine Learning