Affective Signal Processing (ASP) : Unraveling the mystery of emotions

List of Figures
List of Tables
I.   Prologue
1   Introduction
	Introduction
	Affect, emotion, and related constructs
	Affective Computing: A concise overview
	Affective Signal Processing (ASP): A research rationale
	The closed loop model
	Three disciplines
		Human-Computer Interaction (HCI)
		Artificial Intelligence (AI)
		Health Informatics
		Three disciplines, one family
	Outline
2   A review of Affective Computing
	Introduction
	Vision
	Speech
	Biosignals
		A review
		Time for a change
II.   Baseline-free ASP
3   Statistical moments as signal features
	Introduction
	Emotion
	Measures of affect
	Affective wearables
	Experiment
		Participants
		Equipment and materials
		Procedure
	Data reduction
	Results
	Discussion
		Comparison with the literature
		Use in products
4   Time windows and event-related responses
	Introduction
	Data reduction
	Results
		The influence of scene changes
		The film fragments
		Mapping events on signals
	Discussion and conclusion
		Interpreting the signals measured
		Looking back and forth
III.   Bi-modal ASP
5   Emotion models, environment, personality, and demographics
	Introduction
	Emotions
		On defining emotions
		Modeling emotion
	Ubiquitous signals of emotion
	Method
		Participants
		International Affective Picture System (IAPS)
		Digital Rating System (DRS)
	Signal processing
		Signal selection
		Speech signal
		Heart rate variability (HRV) extraction
		Normalization
	Results
		Considerations with the analysis
		The (dimensional) valence-arousal (VA) model
		The six basic emotions
		The valence-arousal (VA) model versus basic emotions
	Discussion
		The five issues under investigation
		Conclusion
6   Static versus dynamic stimuli
	Introduction
	Emotion
	Method
	Preparation for analysis
	Results
		Considerations with the analysis
		The (dimensional) valence-arousal (VA) model
		The six basic emotions
		The valence-arousal (VA) model versus basic emotions
	Static versus dynamic stimuli
	Conclusion
IV.   Towards affective computing
7   Automatic classification of affective signals
	Introduction
	Data set
		Procedure
	Preprocessing
		Normalization
		Baseline matrix
		Feature selection
	Classification results
		k-Nearest Neighbors (k-NN)
		Support vector machines (SVM)
		Multi-Layer Perceptron (MLP) neural network
		Reflection on the results
	Discussion
	Conclusions
8   Two clinical case studies on bimodal health-related stress assessment
	Introduction
	Post-Traumatic Stress Disorder (PTSD)
	Storytelling and reliving the past
	Emotion detection by means of speech signal analysis
	The Subjective Unit of Distress (SUD)
	Design and procedure
	Features extracted from the speech signal
	Results
		Results of the Stress-Provoking Story (SPS) sessions
		Results of the Re-Living (RL) sessions
			Overview of the features
	Discussion
		Stress-Provoking Stories (SPS) study
		Re-Living (RL) study
		Stress-Provoking Stories (SPS) versus Re-Living (RL)
	Reflection: Methodological issues and suggestions
	Conclusions
9   Cross-validation of bimodal health-related stress assessment
	Introduction
	Speech signal processing
		Outlier removal
		Parameter selection
		Dimensionality Reduction
	Classification techniques
		k-Nearest Neighbors (k-NN)
		Support vector machines (SVM)
		Multi-Layer Perceptron (MLP) neural network
	Results
		Cross-validation
		Assessment of the experimental design
	Discussion
	Conclusion
V.   Epilogue
10   Guidelines for ASP
	Introduction
	Signal processing guidelines
		Physical sensing characteristics
		Temporal construction
		Normalization
		Context
	Pattern recognition guidelines
		Validation
		Triangulation
		User identification
	Conclusion
11   Discussion
	Introduction
	Historical reflection
	Hot topics: On the value of this monograph
	Impressions / expressions: Affective Computing's I/O
	Applications: Here and now!
		TV experience
		Knowledge representations
		Computer-Aided Diagnosis (CAD)
	Visions of the future
		Robot nannies
		Digital Human Model
	Conclusion
Bibliography
A   Statistical techniques
	Introduction
	Principal component analysis (PCA)
	Analysis of variance (ANOVA)
	Linear regression models
	k-nearest neighbors (k-NN)
	Artificial neural networks (ANN)
	Support vector machine (SVM)
	Leave-one-out cross validation (LOOCV)
Summary
Samenvatting
Dankwoord
Curriculum Vitae
Publications and Patents: A selection
	Publications
	Patents
SIKS Dissertation Series