RNN-LSTM neural network for predicting fMRI neurofeedback scores from EEG signals
Abstract
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.
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