Independent component analysis of EEG data for EGI system

Anna Gajos 1  and Grzegorz M. Wójcik
  • 1 Laboratory of Neuroinformatics, Maria Curie-Sklodowska University
  • 2 Laboratory of Neuroinformatics, Maria Curie-Sklodowska University
  • 3 Department of Transport and Computer Science, University of Economics and Innovation
Anna Gajos and Grzegorz M. Wójcik
  • Corresponding author
  • Laboratory of Neuroinformatics, Maria Curie-Sklodowska University
  • Department of Transport and Computer Science, University of Economics and Innovation
  • Email
  • Search for other articles:
  • degruyter.comGoogle Scholar

Abstract

Component analysis is one of the most important methods used for electroencephalographic (EEG) signal decomposition, and the so-called independent component analysis (ICA) is commonly used. The main function of the ICA algorithm is to find a linear representation of non-Gaussian data whose elements are statistically independent or at least as independent as possible. There are many commercial solutions for EEG signal acquisition. Usually, together with the EEG, one gets a dedicated software to handle the signal. However, quite often, the software does not provide researchers with all necessary functions. A high-performance, dense-array EGI-EEG system is distributed with the NetStation software. Although NetStation is a powerful tool, it does not have any implementation of the ICA algorithm. This causes many problems for researchers who want to export raw data from the amplifier and then work on it using some other tools such as EEGLAB for MATLAB, as these data are not fully compatible with the EGI format. We will present the C++ implementation of ICA that can handle filtered data from the EGI with better affordability. Our tool offers visualization of raw signal and ICA algorithm results and will be distributed under Freeware license.

  • 1.

    EGI. GeoSource 2.0 Technical Manual, 2011.

  • 2.

    Gajos A, Wojcik GM. Electroencephalographic detection of synesthesia. Ann Univ Mariae Curie Sklodowska Sect Inf 2014;14:43–52.

  • 3.

    Brown GD, Yamada S, Sejnowski TJ. Independent components analysis at the neural cocktail party. Trends Neurosci 2001;24:54–63.

    • Crossref
    • Export Citation
  • 4.

    Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 2007;34:1443–9.

    • Crossref
    • PubMed
    • Export Citation
  • 5.

    Hyvarinen A, Oja E. Independent component analysis: algorithms and applications. Finland: Neural Networks Research Centre, Helsinki University of Technology, 2000.

  • 6.

    Ungureanu M, Bigan C, Strungaru R, Lazarescu V. Independent component analysis applied in biomedical signal processing. Meas Sci Rev 2004;4:1–8.

  • 7.

    Huafu C, Dezhong Y. A composite ICA algorithm and the application in localization of brain activities. Neurocomputing 2004;56:429–34.

    • Crossref
    • Export Citation
  • 8.

    Tadeusiewicz R. Obciążenie psychiczne pracą-nowe wyzwania dla ergonomii pod redakcją Tadeusza Juliszewskiego, Halszki Ogińskiej i Macieja Złowodzki. Symulacyjne modele mózgu jako droga do poznania tajników umysłu. Komitet Ergonomii PAN 2011;23–58.

  • 9.

    Tadeusiewicz R. Using neural networks for simplified discovery of some psychological phenomena. In: Artificial Intelligence and Soft Computing 6114. Lecture Notes in Artificial Intelligence. Berlin/Heidelberg/New York: Springer-Verlag, 2010;104–123.

  • 11.

    Tadeusiewicz R. Informatyka i psychologia w społeczeństwie informacyjnym. Sieci neuronowe i inne systemy neurocybernetyczne jako narzędzia informatyczne o ciekawych zastosowaniach na gruncie psychologii. Wydawnictwa AGH Kraków 2011;49–101.

  • 12.

    Kawala-Janik A, Bauer W, Baranowski J, Podpora M, Schneider P. Implementation of fractional calculus-based methods for the purpose of analysis of EEG signals. International Conference on Cybernetic Modeling of Biological Systems (MCSB) 2015. Bio-Algorithms Med-Syst 2015;11:eA43.

  • 13.

    Mikołajewska E, Mikołajewski D. Integrated IT environment for people with disabilities: a new concept. Cent Eur J Med 2014;9:177–82.

  • 14.

    Mikołajewska E, Mikołajewski D. The prospects of brain-computer interface applications in children. Cent Eur J Med 2014;9:74–9.

  • 15.

    EGI. NetStation Viewer Technical Manual, 2011.

  • 16.

    Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 1999;10:626–34.

    • Crossref
    • PubMed
    • Export Citation
  • 17.

    Kawala-Janik A, Bauer W, Baranowski J. Pilot study on using fractional order calculus-based filtering for the purpose of EEG signals analysis. In: International Brain-Computer Interface (BCI) Meeting 2016.

  • 18.

    Tadeusiewicz R. Computers in psychology and psychology in computer science. In: Proceedings of the 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM) with Applications to Ambient Intelligence and Ubiquitous Systems. IEEE 2010;34–8.

  • 19.

    EGI. NetStation Acquisition Technical Manual, 2011.

  • 20.

    it++ documentation. http://itpp.sourceforge.net/4.3.1.

  • 21.

    Wójcik GM, Mikołajewska E, Mikołajewski D, Wierzgała P, Gajos A, Smolira M. Usefulness of EGI EEG system in brain computer interfaces research. Bio-Algorithms Med-Syst 2013;9:73–9.

Purchase article
Get instant unlimited access to the article.
$42.00
Log in
Already have access? Please log in.


or
Log in with your institution

Journal + Issues

Search