Table of Contents: Introduction to EEG- and speech-based emotion recognition

Introduction to EEG- and speech-based emotion recognition

Main Author:	Abhang, Priyanka A.,
Other Authors:	Gawali, Bharti W.,, Mehrotra, Suresh C.,, ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	Amsterdam : Academic Press is an imprint of Elsevier, [2016]
Physical Description:	1 online resource.
Subjects:	Human-computer interaction. Electroencephalography. Artificial intelligence. Emotions -- Computer simulation. Pattern recognition systems. Context-aware computing.

Table of Contents:

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
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