Department of Biomedical Engineering
Mentor: Tay Netoff, Ph.D., Department of Biomedical Engineering
Project: Seizure Risk Forecasting Using Deep Brain Stimulator Recorded Local Field Potential in The Anterior Nucleus of Thalamus
Jack's research uses long-term brain recordings from brain-implant devices to better understand and predict seizures in people with epilepsy. By applying signal processing and machine learning, he studies slow brain activity patterns that evolve over days to weeks and relate to seizure risk. The goal is to support more personalized and adaptive brain stimulation therapies.
Epilepsy is unpredictable, and that uncertainty can strongly affect daily life. Jack is deeply motivated by the opportunity to turn years of continuous brain data into insights that can help patients and clinicians better understand when and why the brain is vulnerable to seizures. Bridging the state-of-the-art engineering methods with real-world clinical data makes this work both challenging and rewarding.
Minnesota is a leader in neuromodulation research and epilepsy care, and this work contributes to that ecosystem. These approaches could help clinicians and scientists to understand seizure dynamics better, potentially reduce trial-and-error in therapy, improve seizure control, and enhance the quality of life for Minnesotans with epilepsy. In the long term, this research may inform next-generation closed-loop brain stimulation technologies.
Seizure-related brain activity often follows multi-day and multi-week cycles that only become visible in long-term recordings. A key insight from Jack's work is that changes in the strength/amplitude of brain rhythms can carry important seizure-relevant information.
