Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Acknowledgments
List of Acronyms
List of Figures
List of Tables
Abstract
Introduction
I. Background
1. Electroencephalography-Based Brain-Computer Interfaces
1.1. HISTORY
1.2. TAXONOMY
1.3. ARCHITECTURE
1.3.1. Signal Acquisition
1.3.2. Features Extraction
1.3.3. Features Translation
1.4. NON-INVASIVE MEASUREMENT OF NEURAL PHENOMENA
1.4.1. Electroencephalography
1.4.2. Magnetoencephalography
1.4.3. Functional Magnetic Resonance Imaging
1.4.4. Functional Near-Infrared Spectroscopy
1.4.5. Other Techniques
1.5. MEASURING THE ELECTRICAL BRAIN ACTIVITY
1.5.1. Measurand Brain Signals
1.6. PARADIGMS: REACTIVE, PASSIVE, AND ACTIVE
2. Design of Daily-Life Brain-Computer Interfaces
2.1. ACQUISITION SYSTEM
2.2. ELECTRODES
2.2.1. Electrode-Electrolyte Interface and Polarization
2.2.2. Electrode-Skin System
2.2.3. Wet and Dry Electrodes
2.3. CHANNEL MINIMIZATION STRATEGIES
2.4. CHARACTERIZATION OF LOW-COST ELECTROENCEPHALOGRAPHS
2.4.1. Experiments
2.4.2. Data Analysis
2.4.3. Discussion
2.5. CYBERSECURITY AND PRIVACY ISSUES
II. Reactive Brain-Computer Interfaces
3. Fundamentals
3.1. DETECTION OF STEADY-STATE VISUALLY EVOKED POTENTIALS
3.1.1. Physiological Basis
3.1.2. Measurement Setup
3.2. STATEMENT OF THE METROLOGICAL PROBLEM
3.2.1. Requirements
3.2.2. Implementations
3.2.3. Perspectives
3.2.4. Metrological Considerations
3.2.5. Signal Quality
3.2.6. Smart Glasses Characterization
4. SSVEP-Based Instrumentation
4.1. DESIGN
4.2. PROTOTYPE
4.2.1. Augmented Reality Glasses
4.2.2. Single-Channel Electroencephalography
4.2.3. Data Processing
4.3. PERFORMANCE
4.3.1. Frequency Domain
4.3.2. Time Domain
4.3.3. Response Time
4.3.4. Comparison with Literature
5. Case Studies
5.1. INDUSTRIAL MAINTENANCE
5.2. CIVIL ENGINEERING
5.3. ROBOTIC REHABILITATION
III. Passive Brain-Computer Interfaces
6. Fundamentals
6.1. MEASURANDS
6.1.1. Attention in Rehabilitation
6.1.2. Emotional Valence and Human-Machine Interaction
6.1.3. Work-Related Stress
6.1.4. Engagement in Learning and Rehabilitation
6.2. STATE OF THE ART OF EEG MARKER IN PASSIVE BCI
6.2.1. Attention Detection
6.2.2. Emotional Valence Assessment
6.2.3. Stress Monitoring
6.2.4. Engagement Recognition
6.3. STATEMENT OF THE METROLOGICAL PROBLEM
7. EEG-Based Monitoring Instrumentation
7.1. DESIGN
7.1.1. Basic Ideas
7.1.2. Architecture
7.2. PROTOTYPE
7.2.1. Signal Pre-Processing and Features Extraction
7.2.2. Classification
8. Case Studies
8.1. ATTENTION MONITORING IN NEUROMOTOR REHABILITATION
8.1.1. Data Acquisition
8.1.2. Processing
8.1.3. Results and Discussion
8.2. EMOTION DETECTION FOR NEURO-MARKETING
8.2.1. Data Acquisition
8.2.2. Processing
8.2.3. Results and Discussion
8.3. STRESS ASSESSMENT IN HUMAN-ROBOT INTERACTION
8.3.1. Data Acquisition
8.3.2. Processing
8.3.3. Results and Discussion
8.4. ENGAGEMENT DETECTION IN LEARNING
8.4.1. Data Acquisition
8.4.2. Processing
8.4.3. Results and Discussion
8.5. ENGAGEMENT DETECTION IN REHABILITATION
8.5.1. Data Acquisition
8.5.2. Processing
8.5.3. Results and Discussion
IV. Active Brain-Computer Interfaces
9. Fundamentals
9.1. STATEMENT OF THE METROLOGICAL PROBLEM
9.1.1. Motor Imagery
9.1.2. Neurofeedback in Motor Imagery
9.2. DETECTION OF EVENT-RELATED (DE)SYNCHRONIZATION
9.2.1. Neurophysiology of Motor Imagery
9.2.2. Time Course of Event-Related Patterns
10. Motor Imagery-Based Instrumentation
10.1. DESIGN
10.2. PROTOTYPE
10.2.1. Filter Bank
10.2.2. Spatial Filtering
10.2.3. Features Selection
10.2.4. Classification of Mental Tasks
10.3. PERFORMANCE
10.3.1. Benchmark Datasets
10.3.2. Testing the Feature Extraction Algorithm
10.3.3. ERDS Detection
11. Case Studies
11.1. WEARABLE SYSTEM FOR CONTROL APPLICATIONS
11.1.1. Experiments
11.1.2. Discussion
11.2. USER-MACHINE CO-ADAPTATION IN MOTOR REHABILITATION
11.2.1. Experiments
11.2.2. Discussion
Bibliography
Index