Neurofeedback (NF) is a method that allows a subject to learn how to regulate his or her brain activity [Sitaram et al., 2017]. During a training session, the subject will see real-time feedback from his or her brain activity and can use it to perform a task such as motor imagery. To measure brain activity, simultaneous acquisitions with EEG and fMRI provide more effective NF training due to their temporal and spatial complementarity [Perronnet et al., 2017]. However, using MRI is expensive and can be draining for the subject. Therefore, we would like to reduce its use and thus improve the portability of EEG-fMRI neurofeedback. Following the work of [Cury et al., 2020], we propose a method based on a convolutional neural network (CNN). This method consists in learning a model from simultaneous EEG-fMRI acquisitions to predict NF-fMRI scores with EEG signals alone.