Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications
Author: Xiang Zhang and Lina Yao
Publisher finelybook 出版社: WSPC (EUROPE) (September 14, 2021)
Language 语言: English
Print Length 页数: 294 pages
ISBN-10: 1786349582
ISBN-13: 9781786349583
Book 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 Author: decoding an individuals’ brain signals into commands recognizable Author: 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.
