hvEEGNet: a novel deep learning model for high-fidelity EEG reconstruction
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.
Authors
- Giulia Cisotto (ORCID: https://orcid.org/0000-0002-9554-9367)
- Italo Zoppis (ORCID: https://orcid.org/0000-0001-7312-7123)
- Alberto Zancanaro (ORCID: https://orcid.org/0000-0002-5276-7030)
- Sara Manzoni (ORCID: https://orcid.org/0000-0002-6406-536X)
Institutions
- University of Padua (IT)
- University of Milano-Bicocca (IT)
Publication Details
- Journal
- Frontiers in Neuroinformatics
- Published
- 2024-12-20
- DOI
- https://doi.org/10.3389/fninf.2024.1459970
- Citations
- 6
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.06
Funders
- Università degli Studi di Milano
- Dipartimenti di Eccellenza
- Università degli Studi di Milano-Bicocca
- Università degli Studi di Padova