Szczepan Paszkiel - Analysis and Classification of EEG Signals for Brain–Computer Interfaces

Volume 852

Studies in Computational Intelligence

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Polish Academy of Sciences, Warsaw, Poland

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Szczepan Paszkiel

Analysis and Classification of EEG Signals for BrainComputer Interfaces

Szczepan Paszkiel

Department of Biomedical Engineering, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland

ISSN 1860-949X e-ISSN 1860-9503

Studies in Computational Intelligence

ISBN 978-3-030-30580-2 e-ISBN 978-3-030-30581-9

https://doi.org/10.1007/978-3-030-30581-9

Springer Nature Switzerland AG 2020

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Contents

This monograph is a collection of information on the development of braincomputer (BCI) technology with particular focus on data acquisition methods-tools used for human brain activity. Due to universal and easy application, author focused on the use of electroencephalography as an essential method commonly used in measurements for the needs of the development of BCI technology. This method makes it possible to investigate bioelectric activity of neurons by placing electrodes directly on the cortical surface (invasive method) or on the head surface (non-invasive method). The signal received during such implementations is an electroencephalographic signal, in which brain waves oscillations such as: alpha, beta, theta, gamma, lambda band oscillations etc., are separated. However, electroencephalography is not the only method of acquisition used during the realization of solutions within the braincomputer interface technology, therefore, the methods of human brain investigations such as: Magnetoencephalography (MEG), Functional Magnetic Resonance Imaging (FMRI), Positron Emission Tomography (PET), Near Infrared Spectroscopy (NIRS) are discussed in this monograph. The methods of data analysis in the scope of human brain activity, including, among others, statistical methods are described in the following chapters. Also, the Moore-Penrose pseudoinverse as a potential tool for the EEG signal reconstruction is presented. Furthermore, the use of the LORETA method for localization of the EEG signal sources in BCI technology is discussed; it is the method for brain activity imaging based on electroencelographic and magnetoencephalographic records. The monograph also discusses the issue of using neural networks for classification of the changes in the EEG signal based on facial expressions, which was then implemented in practical implementations of the developments based on the research results.

The implementation part of the monograph refers to the authors use of BCI technology in control processes. An idea of controlling a mobile vehicle based on facial expressions, which generating an artifact in the EEG signal adequate to the performance of a given activity, was classified for the needs of the process of controlling a mobile robot. Another example of practical implementation refers to the original use, in the scope of realization methods of control in BCI technology, of LabVIEW environment.

A dynamically developing Virtual Reality (VR) and Augmented Reality (AR) technology has become an impetus for developing the concept of combining AR with BCI technology and then the application of VR technology in correlation with BCI technology. Within the research work, also an exemplary video game in UNITY environment was developed which may be successfully used in a widely developed neurogaming basing on BCI technology, which is described in one of the subsequent chapters.

Within the developments being the outcome of the research work on the braincomputer technology, including identification of the sources of the brain signals generation due to correlation of neuronal cell fractions [], the possibility of implementation of the solutions coming from BCI technology in the scope of the popular IoT technology in the aspect of smart homes is also presented.

The monograph ends with a chapter summing up the obtained results of the research works, with particular focus on their application possibilities in the aspect of carrying out developments in braincomputer technology.

1. Accardo, A., Affinito, M., Carrozzi, M., Bouquet, F.: Use of the fractal dimension for the analysis of electroencephalographic time series. Biol. Cybern. , 339350 (1997) Crossref