5 citations · 15 across the 6 of their papers we have counts for
3 papers · 1 filter
hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications
Giulia Cisotto, Alberto Zancanaro, Italo F. Zoppis +1
With the recent success of artificial intelligence in neuroscience, a number of deep learning (DL) models were proposed for classification, anomaly detection, and pattern recogniti…
vEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders
Alberto Zancanaro, Giulia Cisotto, Italo Zoppis +1
Electroencephalografic (EEG) data are complex multi-dimensional time-series that are very useful in many applications, from diagnostics to driving brain-computer interface systems.…
Comparison of Attention-based Deep Learning Models for EEG Classification
Giulia Cisotto, Alessio Zanga, Joanna Chlebus +3
Objective: To evaluate the impact on Electroencephalography (EEG) classification of different kinds of attention mechanisms in Deep Learning (DL) models. Methods: We compared three…