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Updated: Aug 27, 2025

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

A Personalized User Authentication System Based on EEG Signals.

Christos Stergiadis^1,2, Vasiliki-Despoina Kostaridou¹, Simos Veloudis³

¹Department of Psychology, City College, University of York Europe Campus, 54622 Thessaloniki, Greece.

Sensors (Basel, Switzerland)

|September 23, 2022

Summary

This summary is machine-generated.

This study introduces a novel Electroencephalography (EEG)-based authentication method using machine learning to overcome security issues in conventional biometrics. The approach achieves high accuracy for continuous user authentication, offering a fast and efficient solution.

Keywords:

EEG applied neuroscience biometrics machine learning security user authentication

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Area of Science:

Biometrics and Cybersecurity
Neuroscience and Machine Learning

Background:

Conventional biometrics have limitations in security and continuous authentication.
Brainwave-based authentication offers a promising alternative, addressing drawbacks of traditional methods.

Purpose of the Study:

To propose a data-driven Electroencephalography (EEG)-based authentication method.
To address individual user variability in brainwave patterns.
To identify optimal machine learning algorithms for efficient user authentication.

Main Methods:

Extracted 15 power spectral features from three EEG channels.
Utilized machine learning techniques to find the best classification algorithm per user.
Focused on a data-driven approach to handle individual variability.

Main Results:

Achieved a mean accuracy of 95.6% in user authentication.
Demonstrated reliable granting or denial of access.
Completed the training procedure in under one minute, enabling real-time applications.

Conclusions:

The proposed EEG-based authentication method is highly accurate and efficient.
It offers a viable solution for continuous and secure user authentication.
Machine learning effectively addresses individual variability in brainwave data for authentication.