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Creating a nonparametric brain-computer interface with neural time-series prediction preprocessing.

Damien Coyle1, Thomas M McGinnity, Girijesh Prasad

  • 1Intelligent Syst. Eng. Lab., Ulster Univ, Derry, Nothern Ireland, BT48 7JL, UK. dh.coyle@ulster.ac.uk

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
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This study explores parameter selection for electroencephalogram (EEG)-based brain-computer interfaces (BCIs). A general set of parameters for neural time-series prediction preprocessing (NTSPP) may enable fully nonparametric BCIs.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Brain-computer interfaces (BCIs) often require subject-specific parameter tuning for optimal performance.
  • Existing feature extraction procedures (FEPs) like Hjorth and Barlow, while simple, may not achieve high accuracy.
  • Neural time-series prediction preprocessing (NTSPP) can enhance feature separability but typically needs subject-specific parameters.

Purpose of the Study:

  • To investigate the practicality of using a general set of parameters for NTSPP in EEG-based BCIs.
  • To determine if a fully nonparametric BCI is achievable by optimizing parameter selection.
  • To enhance the performance of simple FEPs like Hjorth and Barlow using NTSPP.

Main Methods:

  • Utilized self-organizing fuzzy neural networks (SOFNNs) as prediction modules (PMs) within the NTSPP framework.

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  • Applied Hjorth- and Barlow-based FEPs combined with linear discriminant analysis (LDA) for classification.
  • Conducted a sensitivity analysis (SA) to identify a general set of NTSPP parameters.

    • Main Results:

    • A general set of NTSPP parameters, identified through SA, showed promising results.
    • The use of a general parameter set potentially eliminates the need for subject-specific tuning.
    • Performance improvements were observed when applying NTSPP with simple FEPs.

    Conclusions:

    • A fully nonparametric BCI may be realizable by employing a general set of NTSPP parameters.
    • The proposed approach simplifies BCI system setup by removing the need for individual calibration.
    • This research contributes to making BCIs more accessible and practical for wider application.