EEG-to-fMRI Prediction for Neurofeedback: Evaluating Regularized Regression and Clustering Approaches

While functional Magnetic Resonance Imaging (fMRI) can provide detailed information regarding the functional activity of the whole brain, its cumbersome experimental setting and high cost prevent its application in ecological conditions. This represents a challenge in the context of neurofeedback therapy. To address this challenge one possible approach is to train a predictor for the localized functional activity using multimodal EEG-fMRI records and utilize that predictor in real-time EEG sessions. Using publicly available multimodal real-time EEG-fMRI data, we present a detailed evaluation of regularized linear regressors both in the context of predicting localized fMRI activity, and to characterize the properties of individual EEG-fMRI runs. Our results indicate that while it is possible to find clusters of similar time-series in which localized brain activity is highly predictable (Pearsons r=0.43), the capacity to train regularized linear regressors able to generalize to new subjects remains limited (r=0.24), highlighting two distinct strategies in EEG-fMRI neurofeedback settings. We characterize the fundamental role for the clustering distance used to identify similar time-series, based both on theoretically-grounded considerations and experimental observations. Our results suggest a clear preference for the cosine metric, instead of the Pearson-based metric utilized in current literature. We further suggest that highly-predictable clusters regular offline paradigms would correspond to high-performing subjects in the context of real-time neurofeedback, and evaluate a regressor-free clustering strategy based on the Log-Euclidean distance of covariance matrices.