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Related Experiment Videos

Person identification from the EEG using nonlinear signal classification.

M Poulos1, M Rangoussi, N Alexandris

  • 1Department of Informatics, University of Piraeus, Greece. marios.p@usa.net

Methods of Information in Medicine
|April 6, 2002
PubMed
Summary
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This study demonstrates that bilinear models applied to ElectroEncephaloGram (EEG) signals significantly improve person identification accuracy compared to linear models. EEG analysis offers a viable method for individual authentication.

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Signal Processing

Background:

  • ElectroEncephaloGram (EEG) signals contain individual-specific information.
  • Non-linear components in EEG signals have been previously investigated.

Purpose of the Study:

  • To compare linear and bilinear models for person identification using EEG features.
  • To identify healthy subjects based on EEG data.

Main Methods:

  • Processing of EEG signals using linear (AR) and bilinear methods.
  • Classification of processed EEG data using an artificial neural network classifier.

Main Results:

  • Bilinear model parameters as features enhance correct classification scores.
  • Improved classification accuracy comes with increased computational complexity.

Related Experiment Videos

  • Results are statistically significant at the 99.5% confidence level.

    • Conclusions:

    • EEG signals contain unique individual information.
    • EEG-based person identification and authentication are feasible.
    • Bilinear modeling offers an effective approach for EEG-based identification.