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Comparison of different methods to suppress muscle artifacts in EEG signals

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Abstract

Independent component analysis (ICA) is an approved method for (e.g., muscle) artifact removal in electroencephalography (EEG). But, as it creates only \(m \le n\) components from n signals, it may fail to clearly separate the artifacts. In order to keep the strengths of ICA and overcome its limitations, we extend ICA by state-space modeling (SSM), thereby enabling \(m > n\). Rather than exploring an optimized choice of the ICA algorithm, the effect of this extension is analyzed. Four methods, low-pass filtering (LPF), ICA, ICA–LPF, and ICA–SSM, are applied, first, to a clean epilepsy EEG segment artificially contaminated by muscle artifacts (MA), thereafter to 7 epilepsy patients’ data. Both by visual assessment by an experienced clinician, and by quantitative measures, ICA–SSM is proven to remove MA better and with less signal distortion than ICA–LPF and much better than pure LPF or ICA.

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Notes

  1. Butterworth filters also have a low phase distortion; even this can be avoided by forward–backward filtering. Comparing both with a linear-phase FIR filter shows no noticeable differences in our result.

  2. Auto-regressive Moving-Average model with orders \(p=4\) and \(q=3\); for details, refer to [23, 24, 29,30,31].

  3. Electrodes C4, T8, CP6, FC6, FC2, CP2 were chosen.

  4. Further controls of optimality and reliability are conducted at this point, not to be detailed here.

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Acknowledgements

This work was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) through Collaborative Research Centers SFB 855 and SFB 1261, and PAK 902. The authors are grateful to Dr. Natia Japaridze for helpful discussions and to the anonymous reviewers for constructive suggestions.

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Authors and Affiliations

  1. Faculty of Engineering, Christian-Albrechts-University of Kiel, Kaiserstr. 2, 24143, Kiel, Germany

    Alina Santillán-Guzmán & Ulrich Heute

  2. Universidad Popular Autónoma del Estado de Puebla, 21 Sur 1103 Barrio Santiago, 72410, Puebla, Mexico

    Alina Santillán-Guzmán

  3. Department of Neuropediatrics, Christian-Albrechts-University of Kiel, Schwanenweg 20, 24105, Kiel, Germany

    Ulrich Stephani

  4. Institute for Medical Psychology and Medical Sociology, Christian-Albrechts-University of Kiel, Preußerstr. 1-9, 24105, Kiel, Germany

    Andreas Galka

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  1. Alina Santillán-Guzmán
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  2. Ulrich Heute
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  3. Ulrich Stephani
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  4. Andreas Galka
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Correspondence to Alina Santillán-Guzmán.

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Santillán-Guzmán, A., Heute, U., Stephani, U. et al. Comparison of different methods to suppress muscle artifacts in EEG signals. SIViP 11, 761–768 (2017). https://doi.org/10.1007/s11760-016-1020-4

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  • DOI: https://doi.org/10.1007/s11760-016-1020-4

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