Nov 16, 2023 · We propose vEEGNet, a DL architecture with two modules, ie, an unsupervised module based on variational autoencoders to extract a latent representation of the ...

Jul 26, 2024 · We propose vEEGNet, a DL architecture with two modules, ie, an unsupervised module based on variational autoencoders to extract a latent representation of the ...

Nov 24, 2023 · In this work, we propose vEEG-Net, a DL architecture with two modules, i.e., an unsupervised module based on variational autoencoders to extract ...

In this work, we propose vEEGNet, a DL architecture with two modules, i.e., an unsupervised module based on variational autoencoders to extract a latent ...

vEEGNet: Learning Latent Representations to Reconstruct EEG Raw Data via Variational Autoencoders. https://doi.org/10.1007/978-3-031-62753-8_7.

Bibliographic details on vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders.

Jan 16, 2024 · vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders. Preprint. Full-text available. Nov 2023.

Two variational autoencoder models, namely vEEGNet-ver3 and hvEEG net, are proposed to target the problem of high-fidelity EEG reconstruction and are found ...

vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders. Alberto Zancanaro, Giulia Cisotto, I ...

vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders. A Zancanaro, G Cisotto, I Zoppis, SL Manzoni. arXiv ...