Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications
- Length: 294 pages
- Edition: 1
- Language: English
- Publisher: Wspc (Europe)
- Publication Date: 2021-09-14
- ISBN-10: 1786349582
- ISBN-13: 9781786349583
- Sales Rank: #6022545 (See Top 100 Books)
Deep Learning for EEG-Based Brain–Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain–Computer Interfaces (BCI) in terms of representations, algorithms and applications. BCI bridges humanity’s neural world and the physical world by decoding an individuals’ brain signals into commands recognizable by computer devices. This book presents a highly comprehensive summary of commonly-used brain signals; a systematic introduction of around 12 subcategories of deep learning models; a mind-expanding summary of 200+ state-of-the-art studies adopting deep learning in BCI areas; an overview of a number of BCI applications and how deep learning contributes, along with 31 public BCI data sets. The authors also introduce a set of novel deep learning algorithms aimed at current BCI challenges such as robust representation learning, cross-scenario classification, and semi-supervised learning. Various real-world deep learning-based BCI applications are proposed and some prototypes are presented. The work contained within proposes effective and efficient models which will provide inspiration for people in academia and industry who work on BCI.
Cover page Title page Copyright Page Preface Contents Part 1: Background 1. Introduction 1.1 Background on the Brain–Computer Interface 1.2 Why Deep Learning? 1.3 Why This Book? 2. Brain Signal Acquisition 2.1 Invasive Approaches 2.1.1 Intracortical Approaches 2.1.2 Electrocorticography 2.2 Noninvasive Approaches 2.2.1 Electroencephalography 2.2.2 Functional Near-infrared Spectroscopy 2.2.3 Functional Magnetic Resonance Imaging 2.2.4 Electrooculography 2.2.5 Magnetoencephalography 2.3 EEG Paradigms 2.3.1 Spontaneous EEG 2.3.2 Evoked Potential 3. Deep Learning Foundations 3.1 Discriminative Deep Learning Models 3.1.1 Multilayer Perceptron 3.1.2 Recurrent Neural Networks 3.1.3 Convolutional Neural Networks 3.2 Representative Deep Learning Models 3.2.1 Autoencoder 3.2.2 Restricted Boltzmann Machine 3.2.3 Deep Belief Networks 3.3 Generative Deep Learning Models 3.3.1 Variational Autoencoder 3.3.2 Generative Adversarial Networks 3.4 Hybrid Models Part 2: Deep Learning-Based BCI and Its Applications 4. Deep Learning-Based BCI 4.1 Intracortical and ECoG 4.2 EEG Potentials 4.2.1 Spontaneous EEG Potentials 4.2.2 Evoked Potentials 4.3 fNIRS 4.4 fMRI 4.5 EOG 4.6 MEG 4.7 Discussion 4.7.1 Discussions on Brain Signals 4.7.2 Discussions on Deep Learning Models 5. Deep Learning-Based BCI Applications 5.1 Health Care 5.2 Smart Environments 5.3 Communication 5.4 Security 5.5 Affective Computing 5.6 Driver Fatigue Detection 5.7 Mental Load Measurement 5.8 Other Applications 5.9 Benchmark Data Sets 5.10 Discussions Part 3: Recent Advances on Deep Learning for EEG-Based BCI 6. Robust Brain Signal Representation Learning 6.1 Overview 6.2 Subject-Dependent 6.2.1 Temporal Representation Learning 6.2.2 Spatial Representation Learning 6.2.3 Graphical Representation Learning 6.2.4 Spatiotemporal Representation Learning 6.2.5 Discussion 6.3 Cross-Subject 6.3.1 Overview 6.3.2 EEG Characteristic Analysis 6.3.3 Representation Learning Framework 6.4 Subject-Independent 6.4.1 Transfer Learning 6.4.2 Intersubject Transfer Learning 7. Cross-Scenario Classification 7.1 Overview 7.2 Attention-Based Classification Across Signal Sources 7.2.1 Overview 7.2.2 Reinforced Selective Attention Model 7.2.3 Discussion 7.3 Attention-Based Classification Across Applications 7.3.1 Overview 7.3.2 Reinforced Attentive CNN 7.3.3 Evaluation Across Applications 7.3.4 Discussion 7.4 Transfer Learning Methods 8. Semi-Supervised Classification 8.1 Generative Methods 8.1.1 Overview 8.1.2 Adversarial Variational Embedding Algorithm 8.1.3 Evaluation 8.1.4 Discussion 8.2 Wrapper Methods 8.2.1 Self-Training 8.2.2 Co-Training 8.2.3 Boosting 8.3 Unsupervised Representations Learning Part 4: Typical Deep Learning for EEG-Based BCI Applications 9. Authentication 9.1 EEG-Based Person Identification 9.1.1 Challenges 9.1.2 EEG Pattern Analysis 9.1.3 Methodology 9.1.4 Discussions 9.2 Person Authentication 9.2.1 Motivations 9.2.2 Methodology 9.2.3 Data Acquisition 10. Visual Reconstruction 10.1 Brain2Object: Printing Your Mind 10.1.1 Brain2Object System 10.1.2 Data Acquisition 10.1.3 Online System 10.1.4 Discussions 10.2 Geometrical Shape Reconstruction 10.2.1 EEG Signal Acquisition 10.2.2 Methodology 10.2.3 Evaluations 10.2.4 Discussions 11. Language Interpretation 11.1 Methodology 11.1.1 Overview 11.1.2 Deep Feature Learning 11.1.3 Feature Adaptation 11.2 Brain-Controlled Typing System 11.3 Discussion 12. Intent Recognition in Assisted Living 12.1 System Overview 12.2 Orthogonal Array Tuning Method 12.2.1 Overview 12.2.2 OATM Workflow 12.3 Deployment 12.3.1 Mind-Controlled Mobile Robot 12.3.2 Mind-Controlled Appliances 13. Patient-Independent Neurological Disorder Detection 13.1 Introduction 13.2 Methodology 13.2.1 Overview 13.2.2 EEG Decomposition 13.2.3 Attention-Based Seizure Diagnosis 13.2.4 Patient Detection 13.2.5 Training Details 13.3 Discussions 14. Future Directions and Conclusion 14.1 Future Directions 14.1.1 General Framework 14.1.2 Subject-Independent Classification 14.1.3 Semi-Supervised and Unsupervised Classification 14.1.4 Hardware Portability 14.2 Conclusion Bibliography Index
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