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

arXiv: https://arxiv.org/abs/2507.12873 · HTML: https://mlsanity.com/paper/2507.12873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Deep Learning for Iris Recognition: A Survey](https://mlsanity.com/paper/2210.05866/) (2022)
- [In-ear EEG biometrics for feasible and readily collectable real-world person authentication](https://mlsanity.com/paper/1705.03742/) (2017)
- [Effect of Auditory Stimuli on Electroencephalography-based Authentication](https://mlsanity.com/paper/2206.14519/) (2022)

## Abstract (arXiv)

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.