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EEG-Based Authentication Across Various Event-Related Potentials (ERPs).

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Summary
This summary is machine-generated.

Electroencephalography (EEG) brainwave patterns offer a novel biometric for secure user authentication. A convolutional neural network (CNN) achieved 99% accuracy, proving EEG

Keywords:
biometric authenticationconvolutional neural network (CNN)electroencephalography (EEG)event-related potentials (ERPs)

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Area of Science:

  • Neuroscience and Cybersecurity
  • Biometric Authentication
  • Machine Learning in Security

Background:

  • Traditional authentication methods face limitations in security and user convenience.
  • Biometric authentication offers a more secure and personalized alternative.
  • Electroencephalography (EEG) presents a novel, non-invasive biometric modality.

Purpose of the Study:

  • To investigate the efficacy of EEG signals for user authentication.
  • To explore brainwave patterns as unique biometric identifiers.
  • To advance cybersecurity through innovative authentication frameworks.

Main Methods:

  • Utilized a public EEG authentication dataset from 38 participants.
  • Elicited event-related potentials (ERPs), specifically P300 and N400.
  • Applied signal preprocessing, ERP, and power spectral density (PSD) feature extraction.
  • Compared machine learning (SVM, RF) and deep learning (CNN) classifiers.

Main Results:

  • The proposed Convolutional Neural Network (CNN) model achieved 99% accuracy.
  • Superior performance was observed in the N400-Faces task.
  • Demonstrated effectiveness in discerning neural signatures from semantic and facial stimuli.

Conclusions:

  • EEG-based biometrics are feasible as a secure and non-invasive authentication method.
  • This approach enhances the resilience of authentication frameworks.
  • Contributes to the development of advanced cybersecurity solutions.