Abstract
In this study, we focus on the electroencephalogram (EEG) as a biometric that can be detected continuously with high confidentiality, and aim to realize the person verification using the evoked EEG when presented with an imperceptible vibration stimulus. In previous studies, the content ratios of the power spectrum in theta (4–8 Hz), alpha (8–13 Hz), and beta (13–43 Hz) wavebands as individual features were derived from the evoked EEG data generated by imperceptible vibration stimulation, and the verification performance was evaluated by Support Vector Machine (SVM). The results showed that the Equal Error Rate (EER) was 28.2%; however, this was not a sufficient verification result. In this paper, for the purpose of improving the verification performance, the weighted (normalized) content ratios are adopted as new features and the verification performance is evaluated. Accordingly, the EER is improved to 17.0%. The verification performance is further improved by changing the feature bandwidth to 6–10 Hz, which contains many spectral components in evoked EEG, and the EER is reduced to 16.4%.
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Kobayashi, H., Nakashima, H., Nakanishi, I. (2023). Performance Improvement of Person Verification Using Evoked EEG by Imperceptible Vibratory Stimulation. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_2
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