Our group develops AI-driven methods to analyze and interpret neural signals such as EEG, iEEG, EMG, and fNIRS. We build scalable machine learning and large-model approaches that learn generalizable representations of brain activity and help decode cognitive states with higher accuracy and reliability.

Our current work focuses on AI for clinical EEG/iEEG, including seizure and HFO detection, data-driven mapping of epileptogenic networks, and developing LLM-inspired models that can interpret neural signals in a more flexible, context-aware way. We also advance brain–computer interfaces and human–machine interaction, integrating neural AI into assistive technologies, diagnostics, and adaptive environments.

We collaborate with clinicians, engineers, and international researchers to translate cutting-edge neural AI into impactful tools for medicine, rehabilitation, and cognitive enhancement.

For collaboration or joining the lab, visit brainu.notion.site.