TY  - BOOK
AU  - Zhang, Xiang
AU  - Yao, Lina
TI  - Deep learning for EEG-based brain-computer interfaces: : representations, algorithms and applications
SN  - 9781786349583
U1  - 612.820285 
PY  - 2022///
CY  - New Jersey
PB  - World Scientific Publishing Company Pvt. Ltd.
KW  - Machine learning
KW  - Brain-computer interfaces
N1  - Contents:
Preface
Background:
Introduction
Brain Signal Acquisition
Deep Learning Foundations
Deep Learning-Based BCI and Its Applications:
Deep Learning-Based BCI
Deep Learning-Based BCI Applications
Recent Advances on Deep Learning for EEG-Based BCI:
Robust Brain Signal Representation Learning
Cross-Scenario Classification
Semi-Supervised Classification
Typical Deep Learning for EEG-Based BCI Applications:
Authentication
Visual Reconstruction
Language Interpretation
Intent Recognition in Assisted Living
Patient-Independent Neurological Disorder Detection
Future Directions and Conclusion
Bibliography
Index
N2  - 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
ER  -