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EEG human biometric authentication using eye blink artefacts

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dc.contributor.supervisor Hlomani, Hlomani
dc.contributor.author Madile, Thabang Teddy
dc.date.accessioned 2022-02-01T07:04:23Z
dc.date.available 2022-02-01T07:04:23Z
dc.date.issued 2021-09
dc.identifier.citation Madile, T. (2021) EEG human biometric authentication using eye blink artefacts, Master's Thesis, Botswana International University of Science and Technology: Palapye. en_US
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/398
dc.description Thesis (MSc Computer Science) --Botswana International University of Science and Technology, 2021 en_US
dc.description.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 xv patterns have high FAR and low FRR. Results also show that human emotions and exercising have no significant impact on the proposed approach. en_US
dc.language.iso en en_US
dc.publisher Botswana International University of Science and Technology (BIUST) en_US
dc.subject Biometric en_US
dc.subject Brainwave en_US
dc.subject EEG authentication en_US
dc.subject Eyeblink en_US
dc.title EEG human biometric authentication using eye blink artefacts en_US
dc.description.level msc en_US
dc.description.accessibility unrestricted en_US
dc.description.department cis en_US


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