An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908454342361088 |
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| author | Avola, Danilo Crocetti, Giancarlo Foresti, Gian Luca Pannone, Daniele Piciarelli, Claudio Ranaldi, Amedeo |
| author_facet | Avola, Danilo Crocetti, Giancarlo Foresti, Gian Luca Pannone, Daniele Piciarelli, Claudio Ranaldi, Amedeo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System Avola, Danilo Crocetti, Giancarlo Foresti, Gian Luca Pannone, Daniele Piciarelli, Claudio Ranaldi, Amedeo Machine Learning 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. |
| title | An Investigation of Ear-EEG Signals for a Novel Biometric Authentication System |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.12873 |