Deep learning for EEG-based brain-computer interfaces: representations, algorithms and applications
"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 phy...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New Jersey
World Scientific
2021
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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"-- |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 1 Online-Ressource (296 Seiten) |
| ISBN: | 9781786349590 1786349590 |
| DOI: | 10.1142/q0282 |
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Datensatz im Suchindex
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| ctrlnum | (ZDB-124-WOP)000q0282 (OCoLC)1286872013 (DE-599)BVBBV047576236 |
| dewey-full | 612.8/20285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
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| doi_str_mv | 10.1142/q0282 |
| format | Electronic eBook |
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| id | DE-604.BV047576236 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:32:04Z |
| indexdate | 2024-07-10T09:15:18Z |
| institution | BVB |
| isbn | 9781786349590 1786349590 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032961723 |
| oclc_num | 1286872013 |
| open_access_boolean | |
| physical | 1 Online-Ressource (296 Seiten) |
| psigel | ZDB-124-WOP |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | World Scientific |
| record_format | marc |
| spelling | Zhang, Xiang Verfasser aut Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications Xiang Zhang, Lina Yao New Jersey World Scientific 2021 1 Online-Ressource (296 Seiten) txt rdacontent c rdamedia cr rdacarrier 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"-- Brain-computer interfaces Machine learning Electronic books Yao, Lina Sonstige oth Erscheint auch als Druck-Ausgabe 9781786349583 Erscheint auch als Druck-Ausgabe 1786349582 https://doi.org/10.1142/q0282 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Zhang, Xiang Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications Brain-computer interfaces Machine learning |
| title | Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications |
| title_auth | Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications |
| title_exact_search | Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications |
| title_exact_search_txtP | Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications |
| title_full | Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications Xiang Zhang, Lina Yao |
| title_fullStr | Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications Xiang Zhang, Lina Yao |
| title_full_unstemmed | Deep learning for EEG-based brain-computer interfaces representations, algorithms and applications Xiang Zhang, Lina Yao |
| title_short | Deep learning for EEG-based brain-computer interfaces |
| title_sort | deep learning for eeg based brain computer interfaces representations algorithms and applications |
| title_sub | representations, algorithms and applications |
| topic | Brain-computer interfaces Machine learning |
| topic_facet | Brain-computer interfaces Machine learning |
| url | https://doi.org/10.1142/q0282 |
| work_keys_str_mv | AT zhangxiang deeplearningforeegbasedbraincomputerinterfacesrepresentationsalgorithmsandapplications AT yaolina deeplearningforeegbasedbraincomputerinterfacesrepresentationsalgorithmsandapplications |