000			02519nam a22002177a 4500
999			_c4502 _d4502
005			20230120130907.0
008			230120b ||||| |||| 00| 0 eng d
020			_a9781786349583
082			_a612.820285 _bZHA
100			_aZhang, Xiang _911501
245			_aDeep learning for EEG-based brain-computer interfaces: _brepresentations, algorithms and applications
260			_bWorld Scientific Publishing Company Pvt. Ltd. _aNew Jersey _c2022
300			_axi, 281 p.
365			_aUSD _b98.00
504			_aContents: 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
520			_aDeep 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.
650			_aMachine learning _92343
650			_aBrain-computer interfaces _911502
700			_aYao, Lina _911503
942			_2ddc _cBK