Introduction to EEG- and Speech-Based Emotion Recognition

 
 
Academic Press
  • 1. Auflage
  • |
  • erschienen am 23. März 2016
  • |
  • 198 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-804531-2 (ISBN)
 

Introduction to EEG- and Speech-Based Emotion Recognition Methods examines the background, methods, and utility of using electroencephalograms (EEGs) to detect and recognize different emotions. By incorporating these methods in brain-computer interface (BCI), we can achieve more natural, efficient communication between humans and computers. This book discusses how emotional states can be recognized in EEG images, and how this is useful for BCI applications. EEG and speech processing methods are explored, as are the technological basics of how to operate and record EEGs. Finally, the authors include information on EEG-based emotion recognition, classification, and a proposed EEG/speech fusion method for how to most accurately detect emotional states in EEG recordings.


    • Provides detailed insight on the science of emotion and the brain signals underlying this phenomenon
    • Examines emotions as a multimodal entity, utilizing a bimodal emotion recognition system of EEG and speech data
    • Details the implementation of techniques used for acquiring as well as analyzing EEG and speech signals for emotion recognition


    Ms. Priyanka Abhang has completed her M.Sc. (IT) (2009). She is presently working as a Ph.D. candidate under the guidance of Dr. Bharti Gawali, Professor in Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS), India. Research area entitles as 'Study and analysis of emotion recognition through EEG images and speech processing.
    • Englisch
    • San Diego
    • |
    • USA
    Elsevier Science
    • 9,80 MB
    978-0-12-804531-2 (9780128045312)
    0128045310 (0128045310)
    weitere Ausgaben werden ermittelt
    • 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
    • C
    • D
    • E
    • F
    • G
    • H
    • I
    • J
    • K
    • L
    • M
    • N
    • O
    • P
    • Q
    • R
    • S
    • T
    • U
    • V
    • W
    • Back Cover

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