An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System

TL;DR

This paper investigates the use of ear-EEG signals for biometric authentication, proposing a deep learning framework that achieves promising accuracy, offering a more practical alternative to traditional scalp EEG systems.

Contribution

It introduces a novel ear-EEG based biometric system utilizing combined temporal and spectral features with deep neural networks, demonstrating its feasibility and potential for real-world applications.

Findings

Achieved 82% accuracy in subject identification.

Demonstrated ear-EEG as a practical biometric modality.

Validated on the only available ear-EEG dataset.

Abstract

This work explores the feasibility of biometric authentication using EEG signals acquired through in-ear devices, commonly referred to as ear-EEG. Traditional EEG-based biometric systems, while secure, often suffer from low usability due to cumbersome scalp-based electrode setups. In this study, we propose a novel and practical framework leveraging ear-EEG signals as a user-friendly alternative for everyday biometric authentication. The system extracts an original combination of temporal and spectral features from ear-EEG signals and feeds them into a fully connected deep neural network for subject identification. Experimental results on the only currently available ear-EEG dataset suitable for different purposes, including biometric authentication, demonstrate promising performance, with an average accuracy of 82\% in a subject identification scenario. These findings confirm the potential of ear-EEG as a viable and deployable direction for next-generation real-world biometric systems.