Research Field

Brain-Computer Interface

We develop advanced Brain-Computer Interfaces (BCIs) designed to bridge the gap between complex neural activity and external computational systems. Moving beyond traditional decoding, our research focuses on bi-directional interactions between brain and computer, which include to modulate the brain using computer programs. This technology utilize cutting-edge AI/ML to accurately interpret and guide neural states. By extracting robust features from noisy, real-world neural signals, we aim to build highly reliable BCI solutions as therapeutics.

Neural Dynamics 

Understanding the brain requires looking beyond static snapshots to analyze its continuous, time-varying behavior. In this research thrust, we approach neural networks fundamentally as complex, dynamic systems. By applying rigorous engineering principles, particularly advanced system identification techniques, we model the underlying spatiotemporal trajectories of neural signals. Uncovering these hidden dynamical rules allows us to pinpoint the exact shifts and anomalies in neural circuitry that signal the onset of neurological disorders, establishing a quantitative foundation for clinical diagnosis.

Neural Control

Building upon our dynamic models, we design precision closed-loop control architectures for therapeutic intervention. We translate classical and modern control engineering theories—such as feedback loop optimization and robust control—into the biological domain to engineer targeted neuromodulation strategies. By continuously monitoring a patient's neural state and delivering precisely calculated, real-time stimulation, our goal is to actively steer abnormal neural trajectories back to healthy baselines, providing engineered, personalized treatments for complex neurological conditions.