Analysis and Classification of EEG Signals for Brain–Computer Interfaces, 1st ed. 2020 Studies in Computational Intelligence Series, Vol. 852

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.

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

Presents a wealth of information on the development of brain–computer (BCI) technology with a particular focus on data acquisition methods and tools used for analyzing human brain activity Highlights recent research on the analysis and classification of EEG signals for brain–computer interfaces Covers research work on brain–computer technology, including identification of the sources of brain signals generated due to the correlation of neuronal cell fractions