Deep learning for EEG-Based Brain-Computer Interfaces : representations, algorithms and applications / Xiang Zhang, Harvard University, USA, Lina Yao, University of New South Wales, Australia.
Material type: TextPublisher: New Jersey : World Scientific, ©2022Description: xi, 281 pages cm 23 cmContent type:- text
- unmediated
- volume
- 9781786349583 (hardback)
- 612.820285 23
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
Books | Junaid Zaidi Library, COMSATS University Islamabad Ground Floor | Books | 612.820285 ZHA-D 63328 (Browse shelf(Opens below)) | Checked out | Hardback | 11/23/2024 | 10001000063328 |
Browsing Junaid Zaidi Library, COMSATS University Islamabad shelves, Shelving location: Ground Floor, Collection: Books Close shelf browser (Hides shelf browser)
599.9 STA-B 63120 Biological anthropology : the natural history of humankind / | 611.018 GAR-C 52858 Concise histology / | 611.8 PAT-T 56752 A textbook of neuroanatomy / | 612.820285 ZHA-D 63328 Deep learning for EEG-Based Brain-Computer Interfaces : representations, algorithms and applications / | 616.07 CRO-I 63466 An introduction to human disease : pathology and pathophysiology correlations / | 616.07 CRO-I 63467 An introduction to human disease : a student workbook / | 616.079 CHA-E 56800 Essentials of clinical immunology / |
Includes bibliographical references and index.
"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"-- Provided by publisher.
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