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Brain-Computer Interface for EEG-Based Authentication: Advancements and Practical Implications.

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Summary
This summary is machine-generated.

Electroencephalogram (EEG)-based authentication offers a secure alternative to traditional methods. A CNN model achieved 99% accuracy, demonstrating the potential of EEG for robust digital security.

Keywords:
authenticationbrain–computer interface (BCI)convolutional neural networks (CNN)electroencephalography (EEG)event-related potentials (ERP)

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Area of Science:

  • Neuroscience
  • Computer Science
  • Cybersecurity

Background:

  • Traditional authentication methods face significant security vulnerabilities.
  • Emerging biometric technologies are crucial for enhancing digital security.
  • Electroencephalogram (EEG) signals present a novel biometric modality.

Purpose of the Study:

  • To systematically review and experimentally evaluate EEG-based authentication systems.
  • To assess the feasibility, limitations, and scalability of EEG authentication.
  • To compare the performance of various machine learning models for EEG authentication.

Main Methods:

  • Systematic literature review of EEG authentication.
  • Experimental data collection from nine subjects using diverse approaches.
  • Implementation and evaluation of Convolutional Neural Network (CNN), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) classifiers.

Main Results:

  • The CNN model achieved the highest accuracy at 99%.
  • RF and GB classifiers demonstrated strong performance with 94% and 93% accuracy, respectively.
  • SVM and KNN classifiers showed significantly lower effectiveness in capturing EEG data complexities.

Conclusions:

  • EEG-based authentication systems show significant potential for enhancing digital security.
  • These systems offer a promising, robust, and user-friendly alternative to traditional authentication methods.
  • Advanced machine learning models like CNN are highly effective for EEG signal processing in authentication.