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EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels.

Ying Zeng1,2, Qunjian Wu3, Kai Yang4

  • 1Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610000, China. yingzeng@uestc.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a novel face and electroencephalogram (EEG) authentication method. It achieves high accuracy (94.26%) in seconds, demonstrating robust identity verification using brain signals.

Keywords:
EEGface imagegenetic algorithmidentity authenticationrapid serial visual presentation

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Area of Science:

  • Neuroscience
  • Biometrics
  • Computer Science

Background:

  • Electroencephalogram (EEG) signals are increasingly used for identity authentication.
  • Combining EEG with other biometric traits can enhance authentication specificity and stability.

Purpose of the Study:

  • To propose a novel face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication.
  • To investigate event-related potential (ERP) components induced by self-face and non-self-face stimuli.
  • To develop an effective and robust EEG-based identity authentication method.

Main Methods:

  • A face image-based RSVP paradigm combining face and EEG biometrics.
  • Investigation of ERP components to self-face and non-self-face stimuli.
  • Development of an authentication method using Hierarchical Discriminant Component Analysis (HDCA) and Genetic Algorithm (GA) for subject-specific models with optimized channels.

Main Results:

  • Significant differences in ERP components were found between self-face and non-self-face stimuli.
  • The proposed method achieved an average authentication accuracy of 94.26% within 6 seconds.
  • The method maintained an average accuracy of 88.88% over a 30-day interval, demonstrating robustness and stability.

Conclusions:

  • The proposed framework for EEG-based identity authentication is effective, robust, and stable over time.
  • Combining face and EEG biometrics provides specific and stable traits for reliable authentication.
  • The HDCA and GA-based approach allows for subject-specific models with optimized channel selection.