Machine learning of EEG to help diagnose epilepsy: predicting functional connectivity from structural connectivity
I had the opportunity through PURM to participate in computational neurology research as part of the Center for Neuroengineering and Therapeutics. I worked with Dr. Kathryn Davis and Andy Revell on a project predicting brain functional connectivity from structural connectivity using brain network analysis and machine learning. I focused on creating pipelines to automatically generate brain network features, plot their distributions, and organize the data structure into a feature matrix to fit a random forest pipeline.
Throughout the summer, I learned about EEG, diffusion imaging, brain network analysis, data visualization, and machine learning, which I had previously known nothing about, but was interested in. I found the experience to be extremely rewarding. It not only helped me gain valuable research experience and improve my computational skills, but also gave me more confidence in my abilities and inspired me to continue exploring computational neuroscience. As someone interested in an interdisciplinary approach to using technology for societal impact, I really enjoyed the translational aspects of the lab. I hope to take what I have learned from this lab into future endeavors to better understand and combat society’s urgent problems.