Abstract
Recently, electroencephalogram (EEG) has been used as a biometric modality. An EEG-based biometric system allows an automatic recognition of people based on their EEG signals. The quantity and quality of identity information extracted from EEG determine the performance of the EEG-based biometric system. In this paper, we evaluate the loss in identity information through different signal segmentation scenarios using Autoregressive model and K-Nearest Neighbor classifier. Our objective is to find some criteria linked to data segmentation allowing to reduce as far as possible the simulated loss of identity information. Experiments were conducted on EEG publicly available datasets collected in resting state for both opened and closed eyes. Results show that overlapped segmentation with longer segments’ length stands the best to the simulated loss favoring larger percentages of overlap.
This work has been supported by the National Natural Science Foundation of China under grant 61572091.
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Boubakeur, M.R., Wang, G., Liu, K., Benatchba, K. (2019). Evaluation of Identity Information Loss in EEG-Based Biometric Systems. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_20
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DOI: https://doi.org/10.1007/978-3-030-37078-7_20
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