Analysis and Classification of EEG Signals for Brain–Computer Interfaces (Studies in Computational Intelligence, 852)
Language: English
Published by Springer, 2019
Series: Book 359 of 538 - Studies in Computational Intelligence
- Hardcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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- Title
- Analysis and Classification of EEG Signals for Brain–Computer Interfaces (Studies in Computational Intelligence, 852)
- Author
- Paszkiel
- Publisher
- Springer
- Publication year
- 2019
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3030305805
- ISBN 13
- 9783030305802
- Series
- Book 359 of 538: Studies in Computational Intelligence
This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain–computer interfaces. In addition, it offers a wealth of information, ranging from the description of data acquisition methods in the field of human brain work, to the use of Moore–Penrose pseudo inversion to reconstruct the EEG signal and the LORETA method to locate sources of EEG signal generation for the needs of BCI technology.
In turn, the book explores the use of neural networks for the classification of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also includes dedicated implementation chapters on the use of brain–computer technology in the field of mobile robot control based on Python and the LabVIEW environment. In closing, it discusses the problem of the correlation between brain–computer technology and virtual reality technology."Synopsis" may belong to another edition of this title.
From the Back Cover
This book addresses the problem of EEG signal analysis and the need to classify it for practical use in many sample implementations of brain–computer interfaces. In addition, it offers a wealth of information, ranging from the description of data acquisition methods in the field of human brain work, to the use of Moore–Penrose pseudo inversion to reconstruct the EEG signal and the LORETA method to locate sources of EEG signal generation for the needs of BCI technology.
In turn, the book explores the use of neural networks for the classification of changes in the EEG signal based on facial expressions. Further topics touch on machine learning, deep learning, and neural networks. The book also includes dedicated implementation chapters on the use of brain–computer technology in the field of mobile robot control based on Python and the LabVIEW environment. In closing, it discusses the problem of the correlation between brain–computer technology and virtual reality technology."About the title" may belong to another edition of this title.
Ria Christie Collections
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