An Attention-based Architecture for EEG Classification

Italo Zoppis, Alessio Zanga, Sara Manzoni, Giulia Cisotto, Giulia Cisotto, Angela Morreale, Fabio Stella, Giancarlo Mauri

2020

Abstract

Emerging studies in the deep learning community focus on techniques aimed to identify which part of a graph can be suitable for making better decisions and best contributes to an accurate inference. These researches (i.e., “attentional mechanisms” for graphs) can be applied effectively in all those situations in which it is not trivial to capture dependency between the involved entities while discharging useless information. This is the case, e.g., of functional connectivity in human brain, where rapid physiological changes, artifacts and high inter-subject variability usually require highly trained clinical expertise. In order to evaluate the effectiveness of the attentional mechanism in such critical situation, we consider the task of normal vs abnormal EEG classification using brain network representation of the corresponding EEG recorded signals.

Paper Citation

in Harvard Style

Zoppis I., Zanga A., Manzoni S., Cisotto G., Morreale A., Stella F. and Mauri G. (2020). An Attention-based Architecture for EEG Classification. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS; ISBN 978-989-758-398-8, SciTePress, pages 214-219. DOI: 10.5220/0008953502140219

in Bibtex Style

@conference{biosignals20,
author={Italo Zoppis and Alessio Zanga and Sara Manzoni and Giulia Cisotto and Angela Morreale and Fabio Stella and Giancarlo Mauri},
title={An Attention-based Architecture for EEG Classification},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS},
year={2020},
pages={214-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008953502140219},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS
TI - An Attention-based Architecture for EEG Classification
SN - 978-989-758-398-8
AU - Zoppis I.
AU - Zanga A.
AU - Manzoni S.
AU - Cisotto G.
AU - Morreale A.
AU - Stella F.
AU - Mauri G.
PY - 2020
SP - 214
EP - 219
DO - 10.5220/0008953502140219
PB - SciTePress