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. Key Features: o To the best of our knowledge, this is the first book that presents how to improve BCI systems through deep learning o For data mining researchers who interested in neuroscience, this book presents a new taxonomy of brain signals including 10+ subcategories which are commonly used in BCI, and provide solid foundations of brain signals. For neurological scientists, this book provides a detailed background of deep learning models and the most advanced algorithms/frameworks to deal with BCI challenges. Moreover, this book provides a wide range of real-world application scenarios of human brain signals o This book provides a large number of open-source brain signal datasets, reusable codes, and BCI development advice

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