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ORIGINAL RESEARCH article

Front. Neuroinform.
Volume 18 - 2024 | doi: 10.3389/fninf.2024.1459970
This article is part of the Research Topic Recent Applications of Noninvasive Physiological Signals and Artificial Intelligence View all 6 articles

hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction

Provisionally accepted
Giulia Cisotto Giulia Cisotto 1*Alberto Zancanaro Alberto Zancanaro 2Italo Zoppis Italo Zoppis 1Sara L Manzoni Sara L Manzoni 1
  • 1 Department of Computer Systems and Communication, School of Science, University of Milano-Bicocca, Milano, Lombardy, Italy
  • 2 Department of Information Engineering, School of Engineering, University of Padua, Padua, Veneto, Italy

The final, formatted version of the article will be published soon.

    Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces. We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability. So far, previous works provided good results in either high-fidelity reconstruction of single-channel signals, or poor-quality reconstruction of multi-channel datasets. Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. We tested it on the benchmark Dataset 2a (including 22-channel EEG data from 9 subjects) and we show that it is able to reconstruct all EEG channels with high-fidelity, fastly (in a few tens of epochs), and with high consistency across different subjects. We also investigated the relationship between reconstruction fidelity and the training duration and, using hvEEGNet as an anomaly detector, we spotted some data in the benchmark dataset that are corrupted and never highlighted before. Thus, hvEEGNet could be very useful in several applications where automatic labeling of large EEG dataset is needed and time-consuming. At the same time, this work opens new fundamental research questions about (1) the effectiveness of deep learning models training (for EEG data) and (2) the need for a systematic characterization of the input EEG data to ensure robust modeling.

    Keywords: EEG, VAE, Variational autoencoder, Latent representation, Motor Imagery

    Received: 05 Jul 2024; Accepted: 27 Nov 2024.

    Copyright: © 2024 Cisotto, Zancanaro, Zoppis and Manzoni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Giulia Cisotto, Department of Computer Systems and Communication, School of Science, University of Milano-Bicocca, Milano, 20125, Lombardy, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.