Artifacts extraction from EEG data using the infomax approach

• Autorzy: Górecka J. , Walerjan P.
• Języki publikacji: EN

Abstrakty

EN

The aim of the research is to detect and remove undesired components from EEG data by means of ICA approach. Besides classical signal analysis tools such as adaptive supervised filtering, parametric or non-parametric spectral estimation, time-frequency analysis, the proposed ICA technique can be used for detection of a wide group of artifacts from EEG data. In this paper a new form of nonlinearity implemented in the infomax approach is presented. As it has been proven experimentally, the proposed new sigmoidal function can effectively detect the selected group of artifacts from EEGs and is an useful approach to speed up computations.

Słowa kluczowe

EN

independent component analysis; infomax algorithm; sigmoidal function; EEG data; artifact

PL

analiza komponentów niezależnych; funkcja sigma; dane EEG; artefakt

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2011
• Tom: Vol. 31, no. 4
• Strony: 59--74
• Opis fizyczny: Bibliogr. 17 poz., rys., tab., wykr.

Twórcy

autor

Górecka J.

autor

Walerjan P.

• Departament of Systems Engineering, Signals and Electronics, West Pomeranian University of Technology, 26 kwietnia 10, 71-126 Szczecin, Poland,

Bibliografia

• 1. Majkowski J.: Clinical Electroencephalography (in Polish). Państwowy Zakład Wydawnictw Lekarskich, Warszawa 1986.
• 2. Fisch B. J.: EEG Primer - basic principles of digital and analog eeg, Elsevier 2000.
• 3. Sanei S., Chambers J. A.: EEG Signal Processing. Wiley 2007.
• 4. Viola F. C., Thorne J., Edmonds B., Schneiger T., Eichele T., Debener S.: Semi-automatic identification of independent components representing EEG artifact. Clinical Neurophysiology 2009, 120, 868-877.
• 5. Hyvärinen A., Karhunen J., Oja E.: Independent Component Analysis. Wiley, 2001.
• 6. Jung T.-P., Makeig S., Humphries C., Lee T.-W., McKeown M. J., Iragui V., Sejnowski T. J.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology, Cambridge University Press 2000, 37, 163-178.
• 7. Delorme A., Sejnowski T., Makeig S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 2007, 34, 1443-1449.
• 8. Krishnaveni V., Jayaraman S., Gunasekaran A., Ramadoss K.: Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network, International Journal of Computer Systems Science and Engineering 2006, 1, 322-333.
• 9. Chen X., Wang L., Xu Y.: A Symmetric Orthogonal FastICA Algorithm and Applications in EEG. 5th Intern. Conf. Natural Computation ICNC '09, Tianjin China, 2009, 504-508.
• 10. Fitzgibbon S. P., Powers D. M. W., Pope K. J., Clark C. R.: Removal of EEG Noise and Artifact Using Blind Source Separation. J. Clin. Neuroph. 2007, 24.
• 11. Górecka J.: Using the Independent Component Analysis for removal of undesired components from EEG data (in Polish). Inżynieria Biomedyczna, 2009, 15, 87-90.
• 12. Lee T.-W., Sejnowski T. J.: Independent Component Analysis for mixed sub-gaussian and super-gaussian sources. 4th Joint Symposium on Neural Computation Proc. 1997, 132-139.
• 13. Makeig S. et al.: EEGLAB: ICA Toolbox for Psychophysiological Research, WWW Site, Swartz Center for Computational Neuroscience, Institute of Neural Computation, University of San Diego, California, http://sccn.ucsd.edu/eeglab/.
• 14. Lee T.-W.: Independent Component Analysis. Theory and Applications. Kluwer Academic Publishers, 1998.
• 15. Menon A., Mehrotra K., Mohan Ch. K., Ranka S.: Characterization of a class of sigmoid functions with applications to neural networks. Neural Networks 1996, 9, 819-835.
• 16. Cardoso J. F.: On the stability of source separation algorithms. The Journal of VLSI Signal Processing, Springer 2000, 26, 7-14.
• 17. Choi S., Cichocki A., Amari S.: Flexible Independent Component Analysis, Journal of VLSI Signal Processing - Systems for Signal. Image and Video Technology, Kluwer, 2000, 1-18.