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Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel

Luis Alfredo Moctezuma1, Marta Molinas2

  • 1Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491, Trondheim, Norway. luis.a.moctezuma@ntnu.no.

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Summary

This study introduces a novel electroencephalographic (EEG) biometric system that identifies subjects and rejects intruders using minimal EEG channels. The system achieves high accuracy with as few as three channels, paving the way for practical EEG-based security.

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Area of Science:

  • Biometrics
  • Neuroscience
  • Signal Processing

Background:

  • Biometric systems traditionally rely on physical or behavioral traits.
  • Electroencephalographic (EEG) signals offer a unique physiological signature for identification.
  • Existing EEG biometric methods often require a large number of channels, limiting practicality.

Purpose of the Study:

  • To develop a highly accurate EEG-based biometric system using a minimal subset of EEG channels.
  • To identify subjects and effectively reject unauthorized individuals (intruders).
  • To optimize feature extraction and channel selection for efficiency and performance.

Main Methods:

  • Feature extraction using Discrete Wavelet Transform (DWT) or Empirical Mode Decomposition (EMD) on EEG sub-bands.
  • Computation of instantaneous energy, Teager energy, and fractal dimensions for feature selection.
  • Utilizing the Local Outlier Factor (LOF) algorithm for subject-specific model creation.
  • Employing the Non-dominated Sorting Genetic Algorithm III (NSGA-III) for minimal channel selection, optimizing True Acceptance Rate (TAR) and True Rejection Rate (TRR).

Main Results:

  • Achieved high TAR and TRR using 64 EEG channels.
  • Demonstrated remarkable performance with only three EEG channels, reaching TAR up to [Formula: see text] and TRR up to [Formula: see text] (eyes-open) and TAR [Formula: see text], TRR [Formula: see text] (eyes-closed).
  • NSGA-III optimization showed potential for TARs and TRRs above 0.900 with 1-3 channels using DWT/EMD features.

Conclusions:

  • The proposed approach enables effective subject identification and intruder rejection using a reduced number of EEG channels.
  • The system demonstrates the feasibility of a practical and usable EEG-based biometric system.
  • Further validation on larger datasets is recommended to enhance real-world applicability.