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Analysis and classification of EEG s...

Paszkiel, Szczepan.

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Analysis and classification of EEG signals for brain-computer interfaces

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analysis and classification of EEG signals for brain-computer interfaces/ by Szczepan Paszkiel.
Author:	Paszkiel, Szczepan.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	vi, 132 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Data acquisition methods for human brain activity -- Chapter 3. Brain-computer interface (BCI) technology, etc.
Contained By:	Springer Nature eBook
Subject:	Brain-computer interfaces. -
Online resource:	https://doi.org/10.1007/978-3-030-30581-9
ISBN:	9783030305819

Analysis and classification of EEG signals for brain-computer interfaces
Paszkiel, Szczepan.

Analysis and classification of EEG signals for brain-computer interfaces[electronic resource] /by Szczepan Paszkiel. - Cham :Springer International Publishing :2020. - vi, 132 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8521860-949X ;. - Studies in computational intelligence ;v.852..

Chapter 1. Introduction -- Chapter 2. Data acquisition methods for human brain activity -- Chapter 3. Brain-computer interface (BCI) technology, etc.

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.

ISBN: 9783030305819

Standard No.: 10.1007/978-3-030-30581-9doiSubjects--Topical Terms:

908299
Brain-computer interfaces.

LC Class. No.: QP360.7 / .P37 2020

Dewey Class. No.: 612.80285

Analysis and classification of EEG signals for brain-computer interfaces
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520 $a 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.
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