UID:
almahu_9948174410202882
Umfang:
VI, 132 p.
,
online resource.
Ausgabe:
1st ed. 2020.
ISBN:
9783030305819
Serie:
Studies in Computational Intelligence, 852
Inhalt:
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.
Anmerkung:
Chapter 1. Introduction -- Chapter 2. Data acquisition methods for human brain activity -- Chapter 3. Brain-computer interface (BCI) technology, etc.
In:
Springer eBooks
Weitere Ausg.:
Printed edition: ISBN 9783030305802
Weitere Ausg.:
Printed edition: ISBN 9783030305826
Weitere Ausg.:
Printed edition: ISBN 9783030305833
Sprache:
Englisch
DOI:
10.1007/978-3-030-30581-9
URL:
https://doi.org/10.1007/978-3-030-30581-9
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