activity
20182023
most citedvEEGNet: learning latent representations to reconstruct EEG raw data via variational autoencoders

5 citations · 15 across the 6 of their papers we have counts for

collaborators

7 papers

eess.SP2023★ 2 cited

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…

eess.SP2023★ 5 cited

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.…

cs.CY2021★ 3 cited

ACTA: A Mobile-Health Solution for Integrated Nudge-Neurofeedback Training for Senior Citizens

Giulia Cisotto, Andrea Trentini, Italo Zoppis +5

As the worldwide population gets increasingly aged, in-home telemedicine and mobile-health solutions represent promising services to promote active and independent aging and to con…

eess.SP2020★ 1 cited

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…

cs.DS2020

Complexity Issues of String to Graph Approximate Matching

Riccardo Dondi, Giancarlo Mauri, Italo Zoppis

The problem of matching a query string to a directed graph, whose vertices are labeled by strings, has application in different fields, from data mining to computational biology. S…

cs.DS2018★ 4 cited

Top-k Overlapping Densest Subgraphs: Approximation and Complexity

Riccardo Dondi, Mohammad Mehdi Hosseinzadeh, Giancarlo Mauri +1

A central problem in graph mining is finding dense subgraphs, with several applications in different fields, a notable example being identifying communities. While a lot of effort…

cs.DS2018

Covering with Clubs: Complexity and Approximability

Riccardo Dondi, Giancarlo Mauri, Florian Sikora +1

Finding cohesive subgraphs in a network is a well-known problem in graph theory. Several alternative formulations of cohesive subgraph have been proposed, a notable example being $…