Abstract

Biometrics are getting more popular in the field of security systems and authentication. This is because biometrics are less able to be lost and less able to be stolen or spoofed. EEG-based biometrics are getting more attention recently since they are more resistant to be hacked. This thesis aims to design and implement techniques for the secure authentication of users based on electroencephalogram (EEG) signals. The study was conducted using three datasets: Simultaneous Task EEG workload Dataset, EEG Alpha wave Dataset, and Local Dataset. The first problem addressed is the curse of dimensionality. Four reduced feature sets were used to reduce the dimensions of the systems, namely, cluster map, ANOVA F-Value, logistic regression weights; the cluster map method reached the highest performance with an 82.37% reduction in computation time. The second problem is to reduce the time required to record EEG signals. Different scenarios with different EEG recording durations were tested. The results reveal a temporal threshold, equals to 4 seconds, that balances between performance and implement ability. The third problem is the effect of the auditory stimuli. To do so, six experiments were conducted, native, non-native, and neutral songs. The three songs were conducted using In-Ear and Bone-Conducting headphones. The results show that an increase in the performance of authentication equals 9.27% when using auditory stimuli. Additionally, it shows that using In-Ear or Bone-Conducting auditory stimuli is based on the balance between performance and implement ability. Finally, the performance of EEG-based authentication is independent of the language of auditory stimuli. In conclusion, this thesis contributes to the development of EEG-biometrics by bridging some important gaps in the field.

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https://tesi.univpm.it/bitstream/20.500.12075/468/2/Nibras_Abo_Alzahab_Thesis.pdf