In the context of neurofeedback (NF), simultaneous acquisitions with electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide more effective NF training due to their complementarity [1]. However, the use of MRI is expensive and draining for the subject. Therefore, we would like to reduce its use. Following the work of Cury et al. [2], we propose a method based on a recurrent neural network (RNN) that consists in learning a model from simultaneous EEG-fMRI acquisitions to predict NF-fMRI scores with EEG signals alone.