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Digital Healthcare AI
We develop AI for digital healthcare by analyzing brain and physiological signals from clinical and real-world settings.
Our research focuses on biomarker discovery, predictive modeling, and practical healthcare applications for reliable clinical AI.
EEG-based Bimarker Discovery
EEG-based
Biomarker Discovery
Our lab investigates how the brain functions by analyzing EEG signals and identifying neural patterns associated with disease, cognitive state, and affective condition. Through collaborations with leading hospitals and clinical partners, we study EEG-based biomarkers that provide quantitative insights into neurological and psychological changes. Based on these findings, we develop AI models for robust prediction and interpretation of brain-related conditions in clinical and healthcare settings.
Wearable Physiological Signal Intelligence
Wearable Physiological Signal Intelligence
We develop digital healthcare AI using physiological signals acquired from wearable sensors in everyday environments. Our research focuses on estimating health conditions, detecting early signs of abnormalities, and building practical AI models that can handle complex and noisy biosignals beyond controlled laboratory settings. By translating wearable biosignals into actionable health information, we aim to support continuous monitoring and early detection in real-world healthcare applications.
Brain-Computer Interface
Beyond biomarker discovery, our research extends toward brain-computer interface technologies that connect neural signal understanding with next-generation healthcare systems. By decoding cognitive, affective, and health-related information from brain activity, we explore how EEG-based AI can contribute to more adaptive and precise interfaces for healthcare and assistive applications. This direction broadens our research from signal analysis and prediction toward future interactive neurotechnology.
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