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.

Mauri, G., Stella, F., Morreale, A., Cisotto, G., Manzoni, S., Zanga, A., et al. (2020). An attention-based architecture for EEG classification. In Proc. BIOSTEC 2020 –13th International Joint Conference on Biomedical Engineering Systems and Technologies, Vol. 4, BIOSIGNALS (pp.214-219). SciTePress [10.5220/0008953502140219].

An attention-based architecture for EEG classification

Mauri, Giancarlo;Stella, Fabio;Cisotto, Giulia;Manzoni, Sara;Zanga, Alessio;Zoppis, Italo
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.
slide + paper
Attentional mechanism, Graph attention network, Brain Network, EEG
English
BIOSTEC 2020 – 13th International Joint Conference on Biomedical Engineering Systems and Technologies
2020
P. Gómez Vilda; A. Fred; H. Gamboa
Proc. BIOSTEC 2020 –13th International Joint Conference on Biomedical Engineering Systems and Technologies, Vol. 4, BIOSIGNALS
9789897583988
2020
214
219
none
Mauri, G., Stella, F., Morreale, A., Cisotto, G., Manzoni, S., Zanga, A., et al. (2020). An attention-based architecture for EEG classification. In Proc. BIOSTEC 2020 –13th International Joint Conference on Biomedical Engineering Systems and Technologies, Vol. 4, BIOSIGNALS (pp.214-219). SciTePress [10.5220/0008953502140219].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/270116
Citazioni
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 1
Social impact