000			03124nam a2200421 a 4500
001			000q0282
003			WSP
005			20240731095158.0
007			cr |nu|||unuuu
008			210525s2021 nju ob 001 0 eng d
010			_a 2021024493
040			_aWSPC _beng _cWSPC
020			_a9781786349590 _q(ebook)
020			_a1786349590 _q(ebook)
020			_z9781786349583 _q(hbk.)
020			_z1786349582 _q(hbk.)
050		4	_aQP360.7 _b.Z43 2021
072		7	_aCOM _x042000 _2bisacsh
072		7	_aCOM _x025000 _2bisacsh
072		7	_aCOM _x044000 _2bisacsh
082	0	4	_a612.8/20285 _223
049			_aMAIN
100	1		_aZhang, Xiang. _9178273
245	1	0	_aDeep learning for EEG-based brain-computer interfaces _h[electronic resource] : _brepresentations, algorithms and applications / _cXiang Zhang, Lina Yao.
260			_aNew Jersey : _bWorld Scientific, _c2021.
300			_a1 online resource (296 p.)
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- 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"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
650		0	_aBrain-computer interfaces. _99261
650		0	_aMachine learning. _91831
655		0	_aElectronic books. _93294
700	1		_aYao, Lina. _9178274
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/q0282#t=toc _zAccess to full text is restricted to subscribers.
942			_cEBK
999			_c97729 _d97729