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Related Experiment Video

Updated: Jun 3, 2025

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Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.

Adnan Elahi Khan Khalil1, Jesus Arturo Perez-Diaz1, Jose Antonio Cantoral-Ceballos1

  • 1School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64700, Nuevo Leon, Mexico.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel electroencephalogram (EEG)-based authentication system using a neural network. The system accurately identifies and authenticates users based on brain signals, achieving 97% accuracy for enhanced security.

Keywords:
MLP neural networksP300 potentialselectroencephalogram (EEG)machine learningmulti-factor authenticationuser authenticationuser identification

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biometrics

Background:

  • Growing need for robust security systems due to AI advancements.
  • Increasing interest in brain signal (EEG) analysis for user authentication.
  • Limitations of previous EEG-based methods in achieving high accuracy.

Purpose of the Study:

  • To develop and evaluate an EEG-based user authentication scheme.
  • To utilize P300 potentials and a multi-layer perceptron feedforward neural network (MLP FFNN).
  • To achieve high accuracy in both user identification and authentication.

Main Methods:

  • Utilized electroencephalogram (EEG) signals focusing on P300 potentials.
  • Employed a multi-layer perceptron feedforward neural network (MLP FFNN).
  • Feature extraction using mutual information (MI) on power spectral density (PSD) across five frequency bands.
  • Two-phase process: user identification (multi-class classification) and user authentication (probability assessment).

Main Results:

  • Achieved 97% accuracy in EEG-based user identification.
  • Achieved 97% accuracy in EEG-based user authentication.
  • The scheme accommodates new users without retraining.

Conclusions:

  • The proposed EEG-based authentication scheme offers a reliable and accurate method for safeguarding individual assets.
  • The combination of P300 potentials, MLP FFNN, and MI feature extraction provides robust authentication.
  • This approach represents a significant advancement in biometric security using brain-computer interfaces.