An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System
It addresses the need for user-friendly biometric authentication for everyday use, though it is incremental as it adapts existing EEG methods to a new form factor.
This paper tackled the problem of low usability in EEG-based biometric authentication by proposing a system using ear-EEG signals, achieving an average accuracy of 82% in subject identification.
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.