書籍詳細

書籍詳細




洋書

介護ロボットのための脳コンピュータ・インターフェース

Brain-Computer Interfacing for Assistive Robotics : Electroencephalograms, Recurrent Quantum Neural Networks, and User-Centric Graphical Interfaces

Gandhi, Vaibhav, Ph.D.

Academic Pr 2014/09
236 p. illustrations ; 23 cm   
装丁: Pap   
版表示など: pap.    装丁について
テキストの言語: ENG    出版国: US
ISBN: 9780128015438
KCN: 1021385968
紀伊國屋書店 選定タイトル
標準価格:¥14,732(本体 ¥13,393)   
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納期について
DDC: 612.820285
KDC: F62 ロボット工学
G70 神経内科学・神経科学
F805 ユーザーインターフェース
関連書リスト: SB2745 神経科学 関連書 特集
SB2812B エルゼビア社 心理学・神経科学 関連書 特集
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Annotation

Full-colour text that focuses on brain-computer interfacing for real-time assistive robotic application and details the fundamental issues related with signal processing and the need for alternative approaches.

Full Description

Brain-computer interface (BCI) technology provides a means of communication that allows individuals with severely impaired movement to communicate with assistive devices using the electroencephalogram (EEG) or other brain signals. The practicality of a BCI has been possible due to advances in multi-disciplinary areas of research related to cognitive neuroscience, brain-imaging techniques and human-computer interfaces. However, two major challenges remain in making BCI for assistive robotics practical for day-to-day use: the inherent lower bandwidth of BCI, and how to best handle the unknown embedded noise within the raw EEG. Brain-Computer Interfacing for Assistive Robotics is a result of research focusing on these important aspects of BCI for real-time assistive robotic application. It details the fundamental issues related to non-stationary EEG signal processing (filtering) and the need of an alternative approach for the same. Additionally, the book also discusses techniques for overcoming lower bandwidth of BCIs by designing novel use-centric graphical user interfaces. A detailed investigation into both these approaches is discussed.

Table of Contents

Part 1: Introduction Part 2: Interfacing Brain and Machine Part 3: Fundamentals of Recurrent Quantum Neural Network Part 4: The Proposed GUI Part 5: RQNN Based EEG Enhancement Part 6: GUI and Robot Operation Part 7: Conclusion