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Application of Decomposition Methods in the Filtering of Event-Related Potentials

In: Data Mining for Biomarker Discovery

Author

Listed:
  • Kostas Michalopoulos

    (Technical University of Crete)

  • Vasiliki Iordanidou

    (Technical University of Crete)

  • Michalis Zervakis

    (Technical University of Crete)

Abstract

The processes giving rise to an event-related potential engage several evoked and induced oscillatory components, which reflect phase or nonphase locked activity throughout the multiple trials of an experiment. The separation and identification of such components could not only serve diagnostic purposes but also facilitate the design of brain–computer interface systems. However, the effective analysis of components is hindered by many factors including the complexity of the EEG signal and its variation over the trials. In this chapter, we study several measures for the identification of the nature of independent components and propose a complete methodology for efficient decomposition of the rich information content embedded in the multichannel EEG recordings associated with the multiple trials of an event-related experiment. The efficiency of the proposed methodology is demonstrated through simulated and real experiments.

Suggested Citation

  • Kostas Michalopoulos & Vasiliki Iordanidou & Michalis Zervakis, 2012. "Application of Decomposition Methods in the Filtering of Event-Related Potentials," Springer Optimization and Its Applications, in: Panos M. Pardalos & Petros Xanthopoulos & Michalis Zervakis (ed.), Data Mining for Biomarker Discovery, edition 127, chapter 0, pages 15-29, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-2107-8_2
    DOI: 10.1007/978-1-4614-2107-8_2
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