ECTS - University of Pisa
Modulo di Affective Computing: Lo studente acquisirà conoscenze relative alle emozioni e alle sue correlazioni con i segnali fisiologici. Oltre alle emozioni verranno studiati anche i correlati fisiologici con i disordini mentali
Students will gain knowledge about theories of emotion and mood disorders. Specifically they will learn how to model emotions and how to correlate them to the patterns of physiological signals.
Modulo di Affective Computing: Le conoscenze acquisite verranno verificate attraverso test in itinere e finale
The gained knowledge will be assessed through ongoing tests.
Modulo di Affective Computing: Lo studente sarà in grado di capire le relazioni tra pattern di segnali fisioligici e emozioni, quindi sarà in grado di identificare e caratterizzare le emozioni oltre che capire la neurofisiologia dei disordini mentali.
Students will be able to process physiological data applying advanced linear and nonlinear methods trying to correlate that to the emotional experiences.
Modulo di Affective Computing: Progetto sperimentale finale e prova orale
It is planned a final project with an experimental paradigm.
Modulo di Affective Computing: Capacità di progettare un protocollo sperimentale e definire un paradigma dettagliato
Real experimental data will be collected with a suitable protocol
Modulo di Affective Computing: Progetto sperimentale finale
Assessment will be done through the design a final experimental protocol
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Modulo di Affective Computing: Lezioni frontali e esercitazioni di laboratorio
Frontal lesson and laboratory practice
Modulo di Affective Computing:
Limbic system and hemisphere pre-cortex
Autonomic nervous system: fight or flight and rest and disgest theories
Theories of emotion: how emotions arise
Heart rate variability: methods of analysis and feature extraction
Respiration activity: methods of analysis and feature extraction
Complexity and chaos theory
A special focus on DFA and Entropy
Non-linear methods for feature extraction from physiological signals
Examples of practical applications on non-linear methods in the emotional domain
Time-varying Nonlinear Models of Human Heartbeat Dynamics
Examples of practical applications on point process in the filed of affective computing
Electrodermal activity: models, methods of analysis and feature extraction
Examples of practical applications electrodermal activity and emotions
EEG: methods of analysis and feature extraction
Examples of practical applications on EEG , BCI and emotions
Speech voice processing: models, methods of analysis and feature extraction
Examples of practical applications on emotional speech analysis
Neuroimaging in psychatry
Sleep and dream analysis
Planning and timeline of the assigned projects
Limbic system and hemisphere pre-cortex
Autonomic nervous system: fight or flight and rest and disgest theories
Theories of emotion: how emotions arise
Heart rate variability: methods of analysis and feature extraction
Respiration activity: methods of analysis and feature extraction
Complexity and chaos theory
A special focus on DFA and Entropy
Non-linear methods for feature extraction from physiological signals
Examples of practical applications on non-linear methods in the emotional domain
Time-varying Nonlinear Models of Human Heartbeat Dynamics
Examples of practical applications on point process in the filed of affective computing
Electrodermal activity: models, methods of analysis and feature extraction
Examples of practical applications electrodermal activity and emotions
EEG: methods of analysis and feature extraction
Examples of practical applications on EEG , BCI and emotions
Speech voice processing: models, methods of analysis and feature extraction
Examples of practical applications on emotional speech analysis
Neuroimaging in psychatry
Sleep and dream analysis
Planning and timeline of the assigned projects
Modulo di Affective Computing: Appunti e dispense forniti dal docente
Notes provided by the teacher
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Modulo di Affective Computing: Progetto finale più prova pratica
Practical and oral test
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