Introduction to EEG- and speech-based emotion recognition /

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Abhang, Priyanka A. (Autor), Gawali, Bharti W. (Autor), Mehrotra, Suresh C. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Academic Press is an imprint of Elsevier, [2016]
Temas:
Human-computer interaction.
Electroencephalography.
Artificial intelligence.
Emotions > Computer simulation.
Pattern recognition systems.
Context-aware computing.
Computer simulation.
Digital computer simulation.
Electroencephalography
Pattern Recognition, Automated
Artificial Intelligence
Computer Simulation
�Electroenc�ephalographie.
Intelligence artificielle.
Reconnaissance des formes (Informatique)
Informatique sensible au contexte.
Simulation par ordinateur.
artificial intelligence.
simulation.
COMPUTERS > Computer Literacy.
COMPUTERS > Computer Science.
COMPUTERS > Data Processing.
COMPUTERS > Hardware > General.
COMPUTERS > Information Technology.
COMPUTERS > Machine Theory.
COMPUTERS > Reference.
Digital computer simulation
Computer simulation
Artificial intelligence
Context-aware computing
Emotions > Computer simulation
Human-computer interaction
Pattern recognition systems
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • INTRODUCTION TO EEG- AND SPEECH-BASED EMOTION RECOGNITION
  • INTRODUCTION TO EEG- AND SPEECH-BASED EMOTION RECOGNITION
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • 1
  • Introduction to Emotion, Electroencephalography, and Speech Processing
  • 1.1 INTRODUCTION
  • 1.2 BRAIN PHYSIOLOGY
  • 1.2.1 Major Brain Areas
  • The Brain Stem
  • The Midbrain
  • The Limbic System
  • The Cerebral Cortex
  • The Basal Ganglia
  • The Cerebellum
  • The Cerebrum
  • 1.3 LOBES OF THE BRAIN AND THEIR FUNCTIONS
  • 1.3.1 The Frontal Lobe
  • 1.3.2 The Parietal Lobe
  • 1.3.3 The Temporal Lobe
  • 1.3.4 The Occipital Lobe
  • 1.4 ELECTROENCEPHALOGRAPHY
  • 1.5 HUMAN AUDITORY SYSTEM
  • 1.5.1 Speech Production Mechanism
  • 1.6 SPEECH PROCESSING
  • 1.6.1 Speech Emotion Recognition
  • 1.7 ORGANIZATION OF THE BOOK
  • 1.8 CONCLUSION
  • References
  • 2
  • Technological Basics of EEG Recording and Operation of Apparatus
  • 2.1 INTRODUCTION TO ELECTROENCEPHALOGRAPHY
  • 2.1.1 Brain Waves
  • 2.1.2 Applications of EEG
  • 2.2 MODERN EEG EQUIPMENT
  • 2.2.1 Wired EEG Systems
  • 2.2.1.1 Merits
  • 2.2.1.2 Demerits
  • 2.2.2 Wireless EEG Systems
  • 2.2.2.1 Merits
  • 2.2.2.2 Demerits
  • 2.2.3 Evoked Potentials
  • 2.3 THE EEG 10/20 ELECTRODES PLACEMENT SYSTEM
  • 2.4 EEG ACQUISITION TOOL
  • 2.4.1 EEG Acquire Software
  • 2.4.2 EEG Analysis Software
  • 2.5 ARTIFACTS
  • 2.5.1 Eye Blinks
  • 2.5.2 Eye Movement
  • 2.5.3 Muscular Artifacts
  • 2.5.4 Electrode Artifacts
  • 2.6 SPEECH ACQUISITION AND PROCESSING
  • 2.6.1 Applications of Speech Recognition22
  • 2.6.2 Acquisition Setup
  • 2.7 COMPUTERIZED SPEECH LABORATORY
  • 2.7.1 Key Features of CSL
  • 2.7.2 Facilities Available in CSL
  • 2.7.2.1 Record and Speak Facilities
  • 2.7.2.2 Analytical Tools
  • 2.7.2.3 Other Special Features
  • 2.8 CONCLUSION
  • References
  • 3
  • Technical Aspects of Brain Rhythms and Speech Parameters.
  • 3.1 INTRODUCTION TO BRAIN-WAVE FREQUENCIES
  • 3.1.1 Gamma Waves
  • 3.1.2 Beta Waves
  • 3.1.3 Alpha Waves
  • 3.1.4 Theta Waves
  • 3.1.5 Delta Waves
  • 3.2 SPEECH PROSODIC FEATURES
  • 3.2.1 Acoustic Features for Emotions
  • 3.2.1.1 Prosody-Related Signal Measures
  • 3.2.1.1.1 ENERGY
  • 3.2.1.1.2 PITCH
  • 3.2.1.1.3 FORMANT
  • 3.2.1.1.4 INTENSITY
  • 3.2.1.1.5 LOUDNESS
  • 3.2.1.1.6 DURATION
  • 3.2.1.1.7 SAMPLING RATE
  • 3.2.1.2 Spectral Characteristics Measures
  • 3.2.1.2.1 MEL-FREQUENCY CEPSTRAL COEFFICIENTS
  • 3.2.1.2.2 MEL FILTER BANK ENERGY BASED SLOPE FEATURES
  • 3.2.1.3 Voice Quality-related Measures
  • 3.2.1.3.1 JITTER
  • 3.2.1.3.2 SHIMMER
  • 3.2.1.3.3 HARMONIC TO NOISE RATIO
  • 3.3 SIGNAL PROCESSING ALGORITHMS
  • 3.3.1 Preprocessing Algorithms
  • 3.3.1.1 Common Spatial Patterns (CSP)
  • 3.3.1.2 Independent Component Analysis
  • 3.3.2 Feature Extraction
  • 3.3.2.1 Principal Components Analysis
  • 3.3.2.2 Mel Frequency Cepstral Coefficients for Speech Feature Extraction
  • 3.3.3 Feature Classification
  • 3.3.3.1 Linear Discriminative Analysis
  • 3.3.3.2 Support Vector Machine
  • 3.3.3.2.1 LINEAR CLASSIFICATION
  • 3.3.3.2.2 NON-LINEAR CLASSIFICATION
  • 3.4 CONCLUSION
  • References
  • 4
  • Time and Frequency Analysis
  • 4.1 INTRODUCTION
  • 4.2 FOURIER TRANSFORMATION
  • 4.2.1 Theoretical Background
  • 4.2.2 Aliasing
  • 4.3 GABOR TRANSFORMATION (SHORT-TIME FOURIER TRANSFORMATION)5-7
  • 4.3.1 Theoretical Considerations
  • 4.3.2 Limitations of Gabor Transformation5,6,9
  • 4.4 SHORT-TIME FOURIER TRANSFORMATION
  • 4.4.1 Window Size for Short-Term Spectral Analysis10,11
  • 4.5 WAVELET TRANSFORMATION
  • 4.5.1 Theoretical Background
  • 4.5.1.1 Continuous Wavelet Transformation
  • 4.5.1.2 Dyadic Wavelet Transformation
  • 4.5.1.3 Multiresolution Analysis
  • 4.5.1.4 Discrete Wavelet (Haar) Transformation
  • 4.5.1.5 The Morlet Wavelet.
  • 4.6 TIME DOMAIN VERSUS FREQUENCY DOMAIN ANALYSIS
  • 4.7 EXAMPLES
  • 4.8 CONCLUSION
  • References
  • 5
  • Emotion Recognition
  • 5.1 INTRODUCTION
  • 5.2 MODALITIES FOR EMOTION RECOGNITION SYSTEMS
  • 5.2.1 Physiological
  • 5.2.1.1 Facial Expression
  • 5.2.1.1.1 FEATURES FOR FACIAL EXPRESSIONS
  • 5.2.1.1.2 FACIAL ACTION CODING SYSTEM
  • 5.2.1.1.3 AVAILABLE DATABASES OF FACIAL EXPRESSIONS
  • 5.2.1.1.3.1 ENTERFACE05
  • 5.2.1.1.3.2 COHN-KANADE AU-CODED EXPRESSION DATABASE
  • 5.2.1.1.3.3 MMI FACIAL EXPRESSION DATABASE
  • 5.2.1.1.3.4 JAPANESE FEMALE FACIAL EXPRESSION (JAFFE) DATABASE
  • 5.2.1.1.3.5 RADBOUD FACES DATABASE
  • 5.2.1.2 Body Movement/Gesture
  • 5.2.1.2.1 FEATURES FOR BODY MOVEMENT/GESTURE
  • 5.2.1.2.2 SOFTWARE USED FOR BODY GESTURE/MOVEMENTS
  • 5.2.1.2.2.1 CUBE26
  • 5.2.2 Behavioral
  • 5.2.2.1 Speech
  • 5.2.2.1.1 FEATURES FOR SPEECH SIGNALS
  • 5.2.2.1.2 FEATURES FOR SPEECH
  • 5.2.2.2 Text
  • 5.2.2.2.1 FEATURES EXTRACTED FROM TEXT
  • 5.2.2.2.1.1 GRAPHICS IMAGES (GI) FEATURES
  • 5.2.2.2.1.2 WORDNET-AFFECT FEATURES
  • 5.2.2.2.1.3 OTHER FEATURES
  • 5.2.3 Brain Signals and Imaging
  • 5.2.3.1 Positron Emission Tomography
  • 5.2.3.2 Magnetic Resonance Imaging
  • 5.2.3.3 Magnetoencephalography
  • 5.2.3.4 Functional Magnetic Resonance Imaging
  • 5.2.3.5 NIRS
  • 5.2.3.6 Electroencephalography
  • 5.2.3.6.1 ROUTINE EEG
  • 5.2.3.6.2 SLEEP EEG
  • 5.2.3.6.3 AMBULATORY EEG
  • 5.2.3.6.4 VIDEO TELEMETRY
  • 5.2.3.7 Features for EEG
  • 5.2.3.8 Available Online Database for EEG With Respect to Emotions
  • 5.2.3.8.1 DATASET FOR EMOTION ANALYSIS USING EEG, PHYSIOLOGICAL, AND VIDEO SIGNALS (DEAP)
  • 5.3 CONCLUSION
  • References
  • 6
  • Multimodal Emotion Recognition
  • 6.1 INTRODUCTION
  • 6.1.1 The Need for Multimodal
  • 6.2 MODELS AND THEORIES OF EMOTION
  • 6.2.1 Circumflex Model
  • 6.2.2 Vector Model
  • 6.2.3 Positive Activation-Negative Activation Model.
  • 6.2.4 Plutchik's Model
  • 6.3 PLEASURE, AROUSAL, AND DOMINANCE EMOTIONAL STATE MODEL
  • 6.4 EARLIER EFFORTS IN MULTIMODAL EMOTION RECOGNITION SYSTEMS
  • 6.5 ONLINE DATABASES OF MULTIMODAL EMOTIONS
  • 6.5.1 Surrey Audio-Visual Expressed Emotion Database
  • 6.5.2 Dataset for Emotion Analysis Using EEG, Physiological, and Video Signals
  • 6.5.3 HUMAINE Database
  • 6.5.4 Interactive Emotional Dyadic Motion Capture Database
  • 6.6 ADVANTAGES OF MULTIMODAL APPROACH
  • 6.7 CHALLENGES FOR MULTIMODAL AFFECT RECOGNITION SYSTEMS
  • 6.8 CONCLUSION
  • References
  • 7
  • Proposed EEG/Speech-Based Emotion Recognition System: A Case Study
  • 7.1 INTRODUCTION
  • 7.2 EXPERIMENTAL DATABASE
  • 7.3 EXPERIMENTAL ANALYSIS FOR EEG IMAGES
  • 7.3.1 Active Electrodes From EEG Images for Relaxed, Happy, and Sad Emotional States
  • 7.3.2 Active Regions From EEG Images for Relaxed, Happy, and Sad Emotional States
  • 7.3.3 EEG Images for Relaxed, Happy, and Sad Emotional States
  • 7.3.4 Active Region Size From EEG Images for Relaxed, Happy, and Sad Emotional States
  • 7.4 ANALYSIS OF FEATURE EXTRACTION FROM EEG IMAGES
  • 7.5 EXPERIMENTAL ANALYSIS FOR SPEECH SIGNALS
  • 7.6 CORRELATION OF EEG IMAGES AND SPEECH SIGNALS
  • 7.7 CLASSIFICATION USING LINEAR DISCRIMINATE ANALYSIS
  • 7.8 CONCLUSION
  • References
  • 8
  • Brain-Computer Interface Systems and Their Applications
  • 8.1 INTRODUCTION
  • 8.2 WORKING OF BCI SYSTEMS
  • 8.3 TYPES OF BCI
  • 8.3.1 Invasive BCI
  • Advantages
  • Disadvantages
  • 8.3.2 Partially Invasive BCI
  • Advantages
  • Disadvantages
  • 8.3.3 Noninvasive BCI
  • Advantages
  • Disadvantages
  • 8.4 BCI APPLICATIONS
  • 8.4.1 Prosthetic Control
  • 8.4.2 BCI in Fatigue and Driver Alertness
  • 8.4.3 The P300 Speller18
  • 8.4.3.1 Characteristics of P300
  • 8.4.3.2 Applications of P300
  • 8.4.4 Brain Fingerprinting
  • 8.4.4.1 Brain Fingerprinting Applications.
  • 8.5 CHALLENGES FOR BCI
  • 8.6 CONCLUSION
  • References
  • Index
  • A
  • B
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  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
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  • U
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  • W
  • Back Cover.

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