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Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity

Medical & Biological Engineering & Computing volume 47, pages 413–423 (2009)Cite this article

Abstract

We compared the performance of 22 algorithms for independent component analysis with the aim to find suitable algorithms for applications in the field of surface electrical brain activity analysis. The quality of the separation is assessed with four performance measures: a correlation coefficient based index, a signal-to-interference ratio, a signal-to-distortion-ratio and the computational demand. Artificial data are used consisting of typical electroencephalogram and evoked potentials signal patterns, e.g. spikes, polyspikes, sharp waves and spindles. We evaluate different noise scenarios and the influence of pre-whitening. The comparisons reveal considerable differences between the algorithms, especially concerning the computational load. Algorithms based on the time structure of the data set seem to have advantages in separation quality especially for sine-shaped signals. Derivates of FastICA and Infomax also attain good results. Our results can serve as a reference for selecting a task-specific algorithm to analyze a large number of signal patterns occurring in the surface electrical brain activity.

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Acknowledgments

This work was in part supported by the German Ministry of Science (03IP605) and the German Research Council (DFG Ha 2899/7-1).

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Authors and Affiliations

  1. Biomedical Engineering Department, Faculty of Computer Science and Automation, Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, P. O. Box 100565, 98684, Ilmenau, Thuringia, Germany

    Matthias Klemm & Jens Haueisen

  2. Department of Signal Processing and Pattern Recognition, Biomedical Signal Processing, Faculty of Mathematics and Natural Sciences II, Institute of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Galina Ivanova

Authors
  1. Matthias Klemm
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  2. Jens Haueisen
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  3. Galina Ivanova
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Correspondence to Galina Ivanova.

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Klemm, M., Haueisen, J. & Ivanova, G. Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity. Med Biol Eng Comput 47, 413–423 (2009). https://doi.org/10.1007/s11517-009-0452-1

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