Predicting local fMRI activations from EEG: a Feasibility Study Using Both Classical and Modern Machine Learning Pipelines

Abstract

fMRI’s clinical use is limited by cost, while EEG is more accessible but lacks spatial detail and deep brain coverage. Research aims to predict deep brain activations from combined fMRI and EEG data. We compare classical machine learning and a CNN-transformer pipeline for this mapping across multiple brain regions. As we show, in the first dataset, which is heavily tilted toward visual perception, the activations in the Hippocampus cannot be recovered reliably from EEG, using either pipeline. However, in other regions, predictability is much higher, and in those cases, the deep learning pipeline obtains better predictions. In a second dataset that is based on musical feedback while the visual is blocked, both pipelines yield improved results in the Hippocampus.