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Comparison of sensor selection mechanisms for an ERP-based brain-computer interface.

David Feess1, Mario M Krell, Jan H Metzen

  • 1Robotics Innovation Center, German Research Center for Artificial Intelligence, Bremen, Germany.

Plos One
|July 12, 2013
PubMed
Summary

Reducing electroencephalography (EEG) sensors for brain-computer interfaces (BCIs) is crucial. This study compares sensor selection methods for P300 detection, finding significant reductions possible with optimal strategies.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) using electroencephalography (EEG) often require numerous sensors.
  • Relevant BCI information is distributed across the scalp in complex, variable patterns.
  • Existing sensor selection methods lack comprehensive comparison and baseline evaluation.

Purpose of the Study:

  • To review and compare existing EEG sensor selection methods for BCIs.
  • To propose and evaluate a novel sensor selection criterion.
  • To assess the performance of reduced sensor sets in a passive P300 detection BCI.

Main Methods:

  • Review of multiple EEG sensor selection algorithms.
  • Proposal of a new criterion based on reduced sensor set performance evaluation.

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  • Application to a passive BCI detecting P300 event-related potentials.
  • Cross-session validation of selected sensor constellations.
  • Comparison against random sensor selections and baseline performance.

    • Main Results:

    • Significant differences observed among evaluated sensor selection methods.
    • Identification of a superior method capable of substantial sensor reduction.
    • Demonstration of effective sensor constellation transferability across sessions.
    • Validation of the proposed selection criterion's efficacy.

    Conclusions:

    • Optimal sensor selection can considerably reduce the number of EEG electrodes needed for BCIs.
    • The presented algorithms and evaluation schemes are adaptable to other sensor array classification tasks.
    • This research addresses a key barrier to broader BCI applicability.