BookLi Hu, Zhiguo Zhang, editors.
Summary: This book presents the conceptual and mathematical basis and the implementation of both electroencephalogram (EEG) and EEG signal processing in a comprehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been developed. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies.
Contents:
Intro; Contents; Contributors;
Chapter 1: Introduction; 1.1 Historical Background; 1.2 Why to Write This Book?; 1.3 Who Wrote It and Who to Use?; 1.4 The Organization of the Book; References;
Chapter 2: EEG: Neural Basis and Measurement; 2.1 Neural Basis of EEG; 2.1.1 Volume Conduction and Source Estimation; 2.1.2 EEG, ECoG, and LFP; 2.2 EEG Measurement; 2.2.1 Recording Electrodes; 2.2.1.1 Electrode Type; 2.2.1.2 Electrode Number; 2.2.1.3 Electrode Placement; 2.2.1.4 Impedances; 2.2.1.5 Reference Electrode; 2.2.2 Amplifiers and Converters; 2.2.3 Artefacts; 2.2.4 Suggestions on EEG Measurement 2.2.4.1 Recording Environment2.2.4.2 Parameters of EEG Recording System; 2.2.4.3 Subjects; References;
Chapter 3: Electroencephalography, Evoked Potentials, and Event-Related Potentials; 3.1 Spontaneous EEG Activity; 3.2 Evoked Potentials and Event-Related Responses; 3.3 An Overview of EPs and ERPs; 3.4 Common EP and ERP Components; 3.4.1 Auditory Evoked Potentials; 3.4.2 Visual Evoked Potentials; 3.4.3 Laser Evoked Potentials; 3.4.4 Somatosensory Evoked Potentials; 3.4.5 Steady-State Evoked Potentials; 3.5 Pitfalls and Promise in EP and ERP Studies; References 4.4.4 Oddball Paradigm4.4.5 Sentence Comprehension Paradigm; References;
Chapter 5: EEG Preprocessing and Denoising; 5.1 Artifacts in EEG; 5.1.1 Physiological Artifacts; 5.1.2 Non-physiological Artifacts; 5.2 Montage; 5.3 Filtering; 5.4 Re-referencing; 5.5 Extracting Data Epochs and Removing Baseline Values; 5.6 Removal and Interpolation of Bad Channels; 5.7 Removal of Bad Epochs; 5.8 Removal of EEG Artifacts Using ICA; 5.9 Summary; References;
Chapter 6: Spectral and Time-Frequency Analysis; 6.1 Introduction; 6.2 Spectral Estimation; 6.2.1 Basic Concepts; 6.2.2 Periodogram 6.2.3 The Welchś Method6.2.4 The Multitaper Method; 6.2.5 Autoregressive Method; 6.2.6 Comparison of Spectral Estimation Methods; 6.2.7 Extraction of Spectral Features; 6.3 Time-Frequency Analysis; 6.3.1 Basic Concepts; 6.3.2 Short-Time Fourier Transform (STFT); 6.3.3 Continuous Wavelet Transform (CWT); 6.3.4 Other Commonly Used TFA Methods; 6.3.4.1 Time-Varying AR Model; 6.3.4.2 Time-Frequency Decomposition Methods; 6.3.4.3 Latest Developments; 6.4 Event-Related Synchronization/Desynchronization (ERS/ERD); 6.4.1 Estimation of ERS/ERD; 6.4.2 Baseline Correction and Illustration of ERS/ERD
Chapter 4: ERP Experimental Design4.1 Experimental Design and Cognitive Processes; 4.1.1 Stimulus Properties; 4.1.2 Condition Comparison; 4.1.3 Behavioral Measure; 4.2 Requirements of the ERP Method; 4.2.1 Trial Number; 4.2.2 Stimulus Probability; 4.2.3 Time-Locking; 4.2.4 Time Interval; 4.2.5 Eye and Body Movement Control; 4.3 Confounding Factors; 4.4 Classic Experimental Designs and Corresponding ERP Indexes; 4.4.1 Cue-Target Paradigm; 4.4.2 Face Presentation Paradigm; 4.4.2.1 Stimulus Response Compatibility (SRC) Paradigms; 4.4.2.2 Stop Signal Paradigm; 4.4.3 Gambling Paradigm