Abstract
This study discusses a new biometric system using brain wave signals (EEG). The frequency range of EEG signals is 0–100 Hz, which is categorized into five groups according to their frequency (Delta, Theta, Alpha, Beta, Gamma), however it is noted that all frequency range can degrade in accuracy and recognition speed. The purpose of this study is to explore which frequency range of brain wave signals can be utilized for authentication. In this study, 1,000 data points of EEG signal in group of four channels, F4, P4, C4, and O2 are explored. The practical technique, Independent Component Analysis (ICA) by SOBIRO algorithm is considered clean and separates the individual signals from noise using the technique of supervised neural network for authenticating 20 subjects. From five frequency ranges of EEG signals, it is shown that the best frequency range for the authentication is Delta, which can authenticate 20 subjects within 100 % accuracy.
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Acknowledgments
Thank you to Tayard Desudchit, MD., Neurology & Clinical Neurophysiology, Chulalongkorn Hospital, Thailand who generated the raw EEG data. This research is supported by grants from Sripatum University.
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Tangkraingkij, P. (2016). Significant Frequency Range of Brain Wave Signals for Authentication. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015. Studies in Computational Intelligence, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-319-23509-7_8
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DOI: https://doi.org/10.1007/978-3-319-23509-7_8
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