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Related Experiment Video

Updated: Sep 9, 2025

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

How to Find Effects of Stimulus Processing on Event Related Brain Potentials of Close Others when Hyperscanning Partners

Published on: May 31, 2018

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

Abeer Al-Nafjan¹, Lamia Alahaideb¹, Mashael Aldayel²

¹Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.

Sensors (Basel, Switzerland)

|August 28, 2025

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 authentication convolutional neural network (CNN)electroencephalography (EEG)event-related potentials (ERPs)

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Recording Brain Activity with Ear-Electroencephalography

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Published on: March 31, 2023

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.