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

• URL: http://arxiv.org/abs/2204.07162v1
• Date: Mon, 4 Apr 2022 08:05:33 GMT
• Title: Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG
• Authors: Victor Delvigne, Hazem Wannous, Jean-Philippe Vandeborre, Laurence Ris, Thierry Dutoit
• Abstract summary: We present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG through electrodes, we propose here to also exploit the spatial and temporal information with a transformer-based network. The proposed network has been trained and validated on two public datasets and achieves higher results compared to state-of-the-art models.
• Score: 2.7076510056452654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing interest both in academia and industry. One of the main examples is the increasing number of Brain-Computer Interfaces (BCI) aiming to link brains and computers. In this paper, we present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG through electrodes and process them in recurrent or convolutional based architecture, we propose here to also exploit the spatial and temporal information with a transformer-based network that has already shown its supremacy in many machine-learning (ML) related studies, e.g. machine translation. In addition to this novel architecture, an extensive study on the feature extraction methods, frequential bands and temporal windows length has also been carried out. The proposed network has been trained and validated on two public datasets and achieves higher results compared to state-of-the-art models. As well as proposing better results, the framework could be used in real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD) symptoms or vigilance during a driving assessment.

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