Enhancing brain decoding using attention augmented deep neural networks

Abstract

Neuroimaging techniques have shown to be valuable when studying brain activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), and different deep learning models to perform brain decoding. Specifically, we investigate to which extent one can infer the task performed by a subject based on its MEG data. In order to capture the most relevant features of the signals, self and global attention are incorporated into our models. The obtained results show that the inclusion of attention improves the performance and generalization of the models across subjects.