most citedGenetic algorithm for feature selection of EEG heterogeneous data

35 citations · 81 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2022★ 11 cited

Inner speech recognition through electroencephalographic signals

Francesca Gasparini, Elisa Cazzaniga, Aurora Saibene

This work focuses on inner speech recognition starting from EEG signals. Inner speech recognition is defined as the internalized process in which the person thinks in pure meanings…

eess.SP2022★ 1 cited

The evolution of AI approaches for motor imagery EEG-based BCIs

Aurora Saibene, Silvia Corchs, Mirko Caglioni +1

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathw…

eess.SP2021

Novel EEG-based BCIs for Elderly Rehabilitation Enhancement

Aurora Saibene, Francesca Gasparini, Jordi Solé-Casals

The ageing process may lead to cognitive and physical impairments, which may affect elderly everyday life. In recent years, the use of Brain Computer Interfaces (BCIs) based on Ele…

cs.AI2021★ 34 cited

Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content

Francesca Gasparini, Giulia Rizzi, Aurora Saibene +1

In this paper we present a benchmark dataset generated as part of a project for automatic identification of misogyny within online content, which focuses in particular on memes. Th…

cs.NE2021★ 35 cited

Genetic algorithm for feature selection of EEG heterogeneous data

Aurora Saibene, Francesca Gasparini

The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an o…