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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-09072022-115749


Tipo di tesi
Tesi di laurea magistrale
Autore
RITORTI, FABIANA
URN
etd-09072022-115749
Titolo
Distance-based representation learning for recognizing emotions via EEG data
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
relatore Prof.ssa Vaglini, Gigliola
relatore Prof. Alfeo, Antonio Luca
relatore Dott. Gagliardi, Guido
Parole chiave
  • affective computing
  • representation learning
Data inizio appello
23/09/2022
Consultabilità
Non consultabile
Data di rilascio
23/09/2092
Riassunto
(AC) is a field of biomedical research that builds an "affect model" by analyzing physiological signals. These models can be used to support therapeutic treatments for psychological disorders.
Most approaches involving affective computing are based on the analysis of EEG signals.
The multimodal nature of emotional expressions motivates the use of continuous representations in affective computing.
The representation of features extracted in latent space contains all the important information needed to capture the behavior of the original data; if the latent space is ordered, it is more interpretable.
This thesis focuses on designing and testing a new loss function that maps label ordering to the feature space of representation learning in order to understand whether feature ordering within the latent space is related to the ordering of the original data space.
In this thesis, we use EEG signals from the well-known DEAP and MAHNOB datasets to perform multiclass emotion classification.
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