vEEGNet: A New Deep Learning Model to Classify and Generate EEG

Alberto Zancanaro, Italo Zoppis, Sara Manzoni, Giulia Cisotto, Giulia Cisotto

2023

Abstract

The classification of EEG during motor imagery (MI) represents a challenging task in neuro-rehabilitation. In 2016, a deep learning (DL) model called EEGNet (based on CNN) and its variants attracted much attention for their ability to reach 80% accuracy in a 4-class MI classification. However, they can poorly explain their output decisions, preventing them from definitely solving questions related to inter-subject variability, generalization, and optimal classification. In this paper, we propose vEEGNet, a new model based on EEGNet, whose objective is now two-fold: it is used to classify MI, but also to reconstruct (and eventually generate) EEG signals. The work is still preliminary, but we are able to show that vEEGNet is able to classify 4 types of MI with performances at the state of the art, and, more interestingly, we found out that the reconstructed signals are consistent with the so-called motor-related cortical potentials, very specific and well-known motor-related EEG patterns. Thus, jointly training vEEGNet to both classify and reconstruct EEG might lead it, in the future, to decrease the inter-subject performance variability, and also to generate new EEG samples to augment small datasets to improve classification, with a consequent strong impact on neuro-rehabilitation.

Paper Citation

in Harvard Style

Zancanaro A., Zoppis I., Manzoni S. and Cisotto G. (2023). vEEGNet: A New Deep Learning Model to Classify and Generate EEG. In Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, ISBN 978-989-758-645-3, SciTePress, pages 245-252. DOI: 10.5220/0011990800003476

in Bibtex Style

@conference{ict4awe23,
author={Alberto Zancanaro and Italo Zoppis and Sara Manzoni and Giulia Cisotto},
title={vEEGNet: A New Deep Learning Model to Classify and Generate EEG},
booktitle={Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,},
year={2023},
pages={245-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011990800003476},
isbn={978-989-758-645-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE,
TI - vEEGNet: A New Deep Learning Model to Classify and Generate EEG
SN - 978-989-758-645-3
AU - Zancanaro A.
AU - Zoppis I.
AU - Manzoni S.
AU - Cisotto G.
PY - 2023
SP - 245
EP - 252
DO - 10.5220/0011990800003476
PB - SciTePress