Deep Learning for EEG-based Brain-Computer Interfaces: Representations, Algorithms and Applications

Description

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. Preface Contents Part 1: Background 1. Introduction 2. Brain Signal Acquisition 3. Deep Learning Foundations Part 2: Deep Learning-Based BCI and Its Applications 4. Deep Learning-Based BCI 5. Deep Learning-Based BCI Applications Part 3: Recent Advances on Deep Learning for EEG-Based BCI 6. Robust Brain Signal Representation Learning 7. Cross-Scenario Classification 8. Semi-Supervised Classification Part 4: Typical Deep Learning for EEG-Based BCI Applications 9. Authentication 10. Visual Reconstruction 11. Language Interpretation 12. Intent Recognition in Assisted Living 13. Patient-Independent Neurological Disorder Detection 14. Future Directions and Conclusion Bibliography Index