Analysis and Classification of EEG Signals for Brain–Computer Interfaces (1st ed. 2020) (Studies in Computational Intelligence #852)

By:

Sign Up Now! Already a Member? Log In
You must be logged into Bookshare to access this title. Learn about membership options, or view our freely available titles.

Synopsis
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.
Copyright:
2020

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9783030305819
Related ISBNs:
9783030305802
Publisher:
Springer International Publishing
Date of Addition:
Copyrighted By:
Springer
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Science, Animals, Computers and Internet, Technology
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

Reviews

No Rating Yet