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SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems.

Ashish Ranjan Mishra1, Rakesh Kumar2, Vibha Gupta3

  • 1Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India. ash.cs.recs@gmail.com.

Scientific Data
|July 2, 2024
PubMed
Summary
This summary is machine-generated.

This study combines handwritten signatures with electroencephalography (EEG) brain activity for enhanced biometric security. Multimodal authentication significantly improves system robustness and forgery resistance.

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Area of Science:

  • Biometric Authentication
  • Physiological and Behavioral Biometrics
  • Human-Computer Interaction

Background:

  • Handwritten signatures offer unique behavioral biometrics but are vulnerable to forgery.
  • Existing biometric systems require enhanced security against sophisticated attacks.
  • Noninvasive electroencephalography (EEG) provides unique, difficult-to-replicate physiological data.

Purpose of the Study:

  • To enhance the robustness and security of signature-based biometric systems.
  • To investigate the synergistic benefits of combining handwritten signatures with EEG data.
  • To introduce a novel multimodal dataset for biometric research.

Main Methods:

  • Developed the SignEEG v1.0 dataset comprising EEG signals and handwritten signatures from 70 subjects.
  • Collected data using Emotiv Insight for EEG and Wacom One for signatures.
  • Acquired data across three paradigms: mental imagery, motor imagery, and physical execution of signatures.

Main Results:

  • Multimodal integration of EEG and signatures significantly enhances biometric system robustness.
  • High reliability was achieved even with limited sample sizes.
  • Machine learning classifiers demonstrated the effectiveness of the multimodal approach.

Conclusions:

  • Combining physiological (EEG) and behavioral (signature) modalities offers superior biometric security.
  • The SignEEG v1.0 dataset and methodology provide a strong foundation for future multimodal biometric research.
  • This approach offers a promising solution for secure user identification and verification.