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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

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Person authentication based on eye-closed and visual stimulation using EEG signals.

Hui Yen Yap¹, Yun-Huoy Choo², Zeratul Izzah Mohd Yusoh²

¹Faculty of Information, Science & Technology, Multimedia University (MMU), Melaka, Malaysia. huiyen84@gmail.com.

Brain Informatics

|October 11, 2021

Summary

This summary is machine-generated.

Electroencephalogram (EEG)-based biometrics show promise for user authentication. Consumer-grade EEG devices and specific protocols achieved high accuracy, demonstrating feasibility for practical applications.

Keywords:

Acquisition protocols Authentication Biometrics Brainwaves ERP Electroencephalography

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Area of Science:

Neuroscience
Biometrics
Human-Computer Interaction

Background:

Electroencephalogram (EEG) biometrics leverage unique neural activity for individual identification.
Practical implementation of EEG biometrics faces challenges in usability and user-friendliness.
System evaluation increasingly focuses on user-driven metrics like effectiveness, efficiency, and satisfaction.

Purpose of the Study:

To evaluate the usability and performance of a consumer-grade EEG device for biometric authentication.
To compare two acquisition protocols: eyes-closed (EC) and visual stimulation.
To propose a reasonable acquisition period for effective EEG-based authentication.

Main Methods:

Utilized a self-collected database from eight subjects, with recordings in morning and afternoon sessions.
Employed two tasks: eyes-closed (EC) and visual stimulation, for EEG data acquisition.
Processed EEG signals, computed pairwise correlations, formed feature vectors, and used Support Vector Machine (SVM) for classification.

Main Results:

The eyes-closed (EC) protocol achieved an accuracy of 83.70-96.42%.
The visual stimulation protocol attained an accuracy of 87.64-99.06%.
Both protocols demonstrated high accuracy and reliability with consumer-grade EEG devices.

Conclusions:

Consumer-grade EEG devices are feasible and reliable for biometric authentication.
The proposed acquisition protocols (EC and visual stimulation) show significant potential for practical EEG-based biometric systems.
The study highlights the importance of usability in biometric system design, aligning with user requirements.