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 ...
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 ...
vEEGNet: A New Deep Learning Model to Classify and Generate EEG
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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 ...
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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 ...
The classification of EEG during motor imagery (MI) represents a challenging task in neuro-rehabilitation. In 2016, a deep learning (DL) model called EEGNet ( ...
We propose vEEGNet, a DL architecture with two modules, ie, an unsupervised module based on variational autoencoders to extract a latent representation of the ...
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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 ...
Jun 23, 2024 · I am tyrna build a network to classify EEG Signals from 128 channels and 440 features to classes which correspond to the ImageNet dataset.
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Two variational autoencoder models, namely vEEGNet-ver3 and hvEEG net, are proposed to target the problem of high-fidelity EEG reconstruction and are found ...