Deep Learning for Eeg-Based Brain-Computer Interfaces: Representations, Algorithms and Applications

Xiang Zhang|Lina Yao

ISBN 10: 1786349582 / ISBN 13: 9781786349583

Editore: WSPC (Europe), 2021

Riguardo questo articolo

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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).KlappentextrnrnDeep Learning for EEG-Based Brain-Computer . Codice articolo 404209724

Dati bibliografici

Titolo: Deep Learning for Eeg-Based Brain-Computer ...

Casa editrice: WSPC (Europe)

Data di pubblicazione: 2021

Legatura: Rilegato

Condizione: New

Descrizione articolo

Riassunto:

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 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.

Informazioni sull?autore:

Xiang Zhang is a postdoc fellow at Harvard University, working with Prof. Marinka Zitnik in Machine Intelligence for Medicine and Science (MIMS) lab. He received his PhD degree (in 2020) at School of Computer Science from the University of New South Wales (UNSW) while supervised by Dr Lina Yao. Xiang has a number of publications on the top venues including SIGKDD, ICDM, UbiComp, IJCAI, PerCom, AAAI, ACM TIST, and IEEE Internet of Things Journal. Moreover, Xiang has been awarded Google PhD Fellowship 2018 in Human Computer Interface on a super competitive basis (4 recipients in Australia among 57 recipients global). He was also selected for EPFL Engineering PhD Summit (11 winners out of 200+ applicants).Xiang's research interests lay in graph representation learning, data mining, and deep learning with focusing applications on neurological diagnosis, user authentication, biomedical sciences, health care, and Brain-Computer Interface (BCI).Lina Yao is currently a Scientia Senior Lecturer at School of Computer Science and Engineering, the University of New South Wales (UNSW). She was awarded Australia Research Council (ARC) Discovery Early Career Researcher Award (DECRA) and Inaugural Vice Chancellor's Women's Research Excellence Award (University of Adelaide) in 2015, and Scientia Fellowship (UNSW) in 2020.She currently serves as Associate Editor for ACM Transactions on Sensor Networks (TOSN) and PC members of several most prestigious data mining and machine learning international conferences including NeurIPS, KDD, SIGIR, AAAI, IJCAI, ICDM, and ACM MM. Lina has published around 200 papers on top journal and conferences, along with four books/chapters.