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.
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