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Deep learning for EEG-based brain-computer interfaces[electronic resource] :representations, algorithms and applications /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	612.8/20285
書名/作者:	Deep learning for EEG-based brain-computer interfaces : representations, algorithms and applications // Xiang Zhang, Lina Yao.
作者:	Zhang, Xiang.
其他作者:	Yao, Lina.
出版者:	London : : World Scientific Publishing Europe,, c2022.
面頁冊數:	1 online resource (296 p.)
標題:	Brain-computer interfaces.
標題:	Machine learning.
ISBN:	9781786349590
ISBN:	1786349590
書目註:	Includes bibliographical references and index.
內容註:	Introduction -- Brain signal acquisition -- Deep learning foundations -- Deep learning-based BCI -- Deep learning-based BCI applications -- Robust brain signal representation learning -- Cross-scenario classification -- Semi-supervised classification -- Authentication -- Visual reconstruction -- Language interpretation -- Intent recognition in assisted living -- Patient-independent neurological disorder detection -- Future directions and conclusion.
摘要、提要註:	"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 datasets. 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"--
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/q0282#t=toc

Deep learning for EEG-based brain-computer interfaces[electronic resource] :representations, algorithms and applications /
Zhang, Xiang.

Deep learning for EEG-based brain-computer interfacesrepresentations, algorithms and applications /[electronic resource] :Xiang Zhang, Lina Yao. - London :World Scientific Publishing Europe,c2022. - 1 online resource (296 p.)

Includes bibliographical references and index.

Introduction -- Brain signal acquisition -- Deep learning foundations -- Deep learning-based BCI -- Deep learning-based BCI applications -- Robust brain signal representation learning -- Cross-scenario classification -- Semi-supervised classification -- Authentication -- Visual reconstruction -- Language interpretation -- Intent recognition in assisted living -- Patient-independent neurological disorder detection -- Future directions and conclusion.

"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 datasets. 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"--

Mode of access: World Wide Web.

ISBN: 9781786349590

LCCN: 2021024493Subjects--Topical Terms:

578332
Brain-computer interfaces.

LC Class. No.: QP360.7 / .Z43 2022

Dewey Class. No.: 612.8/20285

Deep learning for EEG-based brain-computer interfaces[electronic resource] :representations, algorithms and applications /
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245 1 0 $a Deep learning for EEG-based brain-computer interfaces $h [electronic resource] : $b representations, algorithms and applications / $c Xiang Zhang, Lina Yao.
260 $a London : $b World Scientific Publishing Europe, $c c2022.
300 $a 1 online resource (296 p.)
504 $a Includes bibliographical references and index.
505 0 $a Introduction -- Brain signal acquisition -- Deep learning foundations -- Deep learning-based BCI -- Deep learning-based BCI applications -- Robust brain signal representation learning -- Cross-scenario classification -- Semi-supervised classification -- Authentication -- Visual reconstruction -- Language interpretation -- Intent recognition in assisted living -- Patient-independent neurological disorder detection -- Future directions and conclusion.
520 $a "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 datasets. 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"-- $c Publisher's website.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Description based on print version record.
650 0 $a Brain-computer interfaces. $3 578332
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700 1 $a Yao, Lina. $3 636229
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/q0282#t=toc