Abstract:
This study proposes a new electroencephalography (EEG) biometric authentication for
humans based on eye blinking signals extracted from brainwaves. The brainwave signal has
been investigated for person authentication over the years because of its difficulties in
spoofing. Due to advancing low-cost EEG hardware equipment, it has recently been
significantly explored. Most studies in brainwave authentication focus on the use of
imagination and mental task to authenticate a subject. Such conventional approaches are
prone to the effect of human emotions and exercising, since this effect alters the brainwave
signal significantly, making such approaches to be less practical in the real world. This study
overcomes this limitation by introducing a new approach, where the effect of eye blinks on
the brainwave is used for authentication. The eye blink effect on the brainwave signal is
considered an artefact in EEG authentication and is usually removed at the pre-processing
stage. However, it holds properties that are ideal for use in authentication, and it is not prone
to human emotions and exercising, thus improving the practicality of brainwave
authentication. Brainwaves were recorded using Neurosky Mindwave Mobile 2 headset. The
NeuroSky blink detection algorithm was used to extract eye blinks and their properties from
the brainwaves. A new authentication algorithm is developed based on three (3) properties:
blink strength, blink time, and the number of blinks at a given time. The proposed
authentication algorithm matches the eye blinking properties stored in a database at the
enrolment stage against the one recorded at the authentication stage. The overall algorithm
results were calculated on a range of 0 – 100. A threshold value of 70 was used to authenticate
a subject. Three (3) experiments were conducted. In the first experiment, we evaluated the
performance of the proposed algorithm. The second experiment evaluated the effect of
emotions (Excitement, Calmness and Stress) on the proposed algorithm. The third experiment
evaluated the effect of exercising on the proposed algorithm. The performance of the
algorithm is measured using False Rejection Rate (FRR), False Acceptance Rate (FAR), and
Accuracy (ACC). Results showed a FAR value of 5% and an FRR value of 1%. The proposed
algorithm achieved an accuracy of 97%. These results show good performance. Results also
indicate that more complex patterns have low FAR and high FRR, while less complicated
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patterns have high FAR and low FRR. Results also show that human emotions and exercising
have no significant impact on the proposed approach.
Teddy, M (2024). EEG human biometric authentication using eye blink artefacts. Afribary. Retrieved from https://track.afribary.com/works/eeg-human-biometric-authentication-using-eye-blink-artefacts
Teddy, Madile "EEG human biometric authentication using eye blink artefacts" Afribary. Afribary, 12 Apr. 2024, https://track.afribary.com/works/eeg-human-biometric-authentication-using-eye-blink-artefacts. Accessed 23 Nov. 2024.
Teddy, Madile . "EEG human biometric authentication using eye blink artefacts". Afribary, Afribary, 12 Apr. 2024. Web. 23 Nov. 2024. < https://track.afribary.com/works/eeg-human-biometric-authentication-using-eye-blink-artefacts >.
Teddy, Madile . "EEG human biometric authentication using eye blink artefacts" Afribary (2024). Accessed November 23, 2024. https://track.afribary.com/works/eeg-human-biometric-authentication-using-eye-blink-artefacts