In order to understand human brain activity, we need non-invasive approaches with high spatial and temporal resolution. Simultaneous EEG-fMRI combines the high sampling rate of EEG with the high spatial resolution of fMRI, but correlating EEG signals with fMRI data using traditional statistics can be difficult, as their relationship is nonlinear and complex. The goal of my project is to design a machine learning approach to overcome this challenge. Specifically, I will build a model that reconstructs an EEG spectrogram from simultaneously recorded fMRI data. I will use this model to understand how spatially-specific brain activity (measured with fMRI) is related to the fast neural activity recorded in the EEG.

I am participating in SuperUROP because it is an amazing opportunity to bolster my research experience, specifically learning how to solve problems that have not been solved before. Additionally, I am interested in the intersection of machine learning and neuroscience, specifically how we can use machine learning to learn more about the brain and cognitive processes. I believe this SuperUROP will help me start answering this question.