Emotion Identification through Biological Signals (EIBS)

The Emotion Identification through Biological Signals (EIBS) course introduces students to to the analysis and identification of human emotions through computational methods and biological signals. It combines theoretical foundations of emotional models with practical approaches involving sensors, data processing, and AI techniques, covering multiple modalities including text, facial expressions, body behaviour, and physiological signals.

Target audience

Postgraduate students with previous coursework on AI (basics) and programming skills

Prerequisites

Basic programming skills and AI (basic courses)

Intended learning outcomes

Students will be able to apply state-of-the-art tools to extract and analyse features of human behaviour.

Teaching and learning methods

The sessions will consist of asynchronous learning content

Assessment

Multiple-choice quizzes

Syllabus

Introduction
Overview of the course objectives, structure, and key concepts in emotion analysis.

Module 1 – Introduction to Emotional Foundations
Fundamental concepts of emotional states, including dimensional and categorical models, and their role in understanding human behaviour.

Module 2 – Primary, Secondary, and Complex Emotional States
Distinction between basic and complex emotions, with reference to key psychological theories and their implications for emotion modelling.

Module 3 – Introduction to Ambient Intelligence
Principles of Ambient Intelligence and context-aware systems, including the transformation from data to knowledge and the role of AI.

Module 4 – Introduction to Sensors for Emotion Analysis
Overview of sensor technologies, their properties and limitations, and the main modalities for capturing emotion-related signals.

Module 5 – Emotion Identification and Analysis: Introduction
General framework for emotion detection, including different channels, invasive vs non-invasive approaches, and multimodal integration.

Module 6 – Emotion Identification and Analysis: Focus on Text
Techniques for emotion and sentiment analysis from textual data using NLP, including methodological approaches and challenges.

Module 7 – Emotion Identification and Analysis: Focus on Face and Body
Methods for analysing emotions through facial expressions and body movements, including feature extraction and classification techniques..

Module 8 – Emotion Identification and Analysis: Focus on Physiological Signals
Methods for analysing emotions through physiological signals, including key modalities such as heart activity, electrodermal response, respiration, muscle activity, and skin temperature, with an overview of sensing techniques and their role in emotion recognition.

Conclusions & Bibliography