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Data Analysis of Human Brain Activity Using MATLAB Environment with EEGLAB

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Analysis and Classification of EEG Signals for Brain–Computer Interfaces

Part of the book series: Studies in Computational Intelligence ((SCI,volume 852))

Abstract

For the purpose of the research work carried out, Emotiv Xavier TestBench environment was used and the biomedical data obtained from it may be used for further processing. The data format (*.edf) is compatible with Toolbox EEGLAB It is an interactive tool within the Matlab environment for processing continuous data connected with EEG, MEG events and other electrophysiological data covering Independent Components Analysis (ICA), time analysis, frequency and artifacts removal. EEGLAB operates under Linux, Unix, Windows and Mac OS X systems. Toolbox EEGLAB is not available in the default version of Matlab, therefore it must be installed.

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References

  1. Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 921 (2004). https://doi.org/10.1016/j.jneumeth.2003.10.009

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  2. Paszkiel, S., Szpulak, P.: Methods of acquisition, archiving and biomedical data analysis of brain functioning, Biomedical Engineering and Neuroscience. In: Hunek, W., Paszkiel, S. (eds.) Proceedings of the 3rd International Scientific Conference on Brain-Computer Interfaces (BCI 2018), Opole, Poland, 13–14 Mar 2018. Advances in Intelligent Systems and Computing (AISC), vol. 720, pp. 158–171. Springer, Berlin (2018). https://doi.org/10.1007/978-3-319-75025-5_15

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Authors and Affiliations

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

    Szczepan Paszkiel

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  1. Szczepan Paszkiel
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Correspondence to Szczepan Paszkiel .

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Paszkiel, S. (2020). Data Analysis of Human Brain Activity Using MATLAB Environment with EEGLAB. In: Analysis and Classification of EEG Signals for Brain–Computer Interfaces. Studies in Computational Intelligence, vol 852. Springer, Cham. https://doi.org/10.1007/978-3-030-30581-9_6

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