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For many years now, understanding the brain mechanism has been a greatresearch subject in many different fields. Brain signal processing andespecially electroencephalogram (EEG) has recently known a growing interestboth in academia and industry. One of the main examples is the increasingnumber of Brain-Computer Interfaces (BCI) aiming to link brains and computers.In this paper, we present a novel framework allowing us to retrieve theattention state, i.e degree of attention given to a specific task, from EEGsignals. While previous methods often consider the spatial relationship in EEGthrough electrodes and process them in recurrent or convolutional basedarchitecture, we propose here to also exploit the spatial and temporalinformation with a transformer-based network that has already shown itssupremacy in many machine-learning (ML) related studies, e.g. machinetranslation. In addition to this novel architecture, an extensive study on thefeature extraction methods, frequential bands and temporal windows length hasalso been carried out. The proposed network has been trained and validated ontwo public datasets and achieves higher results compared to state-of-the-artmodels. As well as proposing better results, the framework could be used inreal applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD)symptoms or vigilance during a driving assessment.

Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG