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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Higher order statistics-based radial basis function network for evoked potentials.

Bor-Shyh Lin1, Bor-Shing Lin, Fok-Ching Chong

  • 1Institute of Electrical Engineering, National Taiwan University, Changhua, Taipei 50307, Taiwan, R.O.C. borshyhlin@ntu.edu.tw

IEEE Transactions on Bio-Medical Engineering
|February 20, 2009
PubMed
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This study introduces a novel higher order statistics-based radial basis function network (RBF) to improve evoked potential (EP) extraction from EEG signals. The method enhances signal-to-noise ratio by reducing gradient noise amplification, crucial for nervous system diagnosis.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neuroscience

Background:

  • Evoked potentials (EPs) are vital for diagnosing nervous system disorders.
  • Extracting EPs from background electroencephalography (EEG) is challenging due to low signal-to-noise ratios.
  • Existing methods like radial basis function networks (RBF) with the least mean square (LMS) algorithm can suffer from gradient noise amplification.

Purpose of the Study:

  • To propose a novel RBF network enhanced with higher order statistics (HOS) for improved EP extraction.
  • To address the gradient noise amplification issue inherent in LMS-based RBF methods.
  • To validate the efficacy of the proposed HOS-RBF method through simulations and human experiments.

Main Methods:

  • Development of a higher order statistics-based radial basis function network (HOS-RBF).

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  • Application of HOS techniques to mitigate gradient noise amplification during RBF adaptation.
  • Comparative analysis with traditional LMS-based RBF methods.

    • Main Results:

    • The HOS-RBF method demonstrated superior performance in extracting EPs compared to standard RBF-LMS.
    • Effective suppression of Gaussian and symmetrically distributed non-Gaussian noise was achieved.
    • Simulations and human experiments confirmed the robustness and accuracy of the proposed technique.

    Conclusions:

    • The proposed HOS-RBF network offers a significant advancement for accurate EP extraction.
    • This method provides a more robust approach for analyzing neural signals in clinical diagnostics.
    • The HOS-RBF technique holds promise for enhancing the diagnosis of neurological conditions.