Abstract
In this paper we present a novel approach to the person identification problem using rhythmic brain activity of spindles from whole night EEG recordings. The proposed system consists of a feature extraction module and a K-NN based classifier. Different types of features from time, frequency and wavelet domain are used to highlight the topographic, temporal, morphological, spectral and statistical discriminative information of sleep spindles. The feature set’s efficacy is exhaustively tested in order to find the most significant descriptors that maximize intra-subject separability. Extensive experiments resulted in the optimal number of sensors and features that must be used to form the subject-specific unique descriptors. The proposed system showed significant identification accuracy of 99% ~ 90% for 2–20 subjects, and not lower than 86% when identifying 28 persons, indicating that this new type of modality should be further investigated to be used in EEG based identification applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sivasankari, N., Muthukumar, A.: A review on recent techniques in multimodal biometrics. In: 2016 International Conference on Computer Communication and Informatics, ICCCI 2016 (2016)
Faridah, Y., Nasir, H., Kushsairy, A.K., Safie, S.I.: Survey multimodal biometric algorithm: a survey (2016)
Del Pozo-Banos, M., Alonso, J.B., Ticay-Rivas, J.R., Travieso, C.M.: Electroencephalogram subject identification: a review (2014)
Thomas, K.P., Vinod, A.P.: Toward EEG-based biometric systems: the great potential of brain-wave-based biometrics. IEEE Syst. Man Cybern. Mag. 3, 6–15 (2017)
Reshmi, K.C., Muhammed, P.I., Priya, V.V., Akhila, V.A.: A novel approach to brain biometric user recognition. Procedia Technol. 25, 240–247 (2016)
Vahid, A., Arbabi, E.: Human identification with EEG signals in different emotional states. In: 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering, ICBME 2016, pp. 242–246 (2017)
Jiralerspong, T., Liu, C., Ishikawa, J.: Identification of three mental states using a motor imagery based brain machine interface. In: IEEE Symposium on Computational Intelligence in Brain Computer Interfaces, pp. 2081–2089 (2014)
Rodrigues, D., Silva, G.F.A., Papa, J.P., Marana, A.N., Yang, X.S.: EEG-based person identification through binary flower pollination algorithm. Expert Syst. Appl. 62, 81–90 (2016)
Kaur, B., Singh, D.: Neuro signals: a future biomertic approach towards user identification. In: Proceedings 7th International Conference on Cloud Computing, Data Science and Engineering, pp. 112–117 (2017)
Iber, C., Ancoli-Israel, S., Chesson, A.: The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications (2007)
‘t Wallant, D.C., Maquet, P., Phillips, C.: Sleep spindles as an electrographic element: description and automatic detection methods. Neural Plast. 2016 (2016)
Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. (1970)
O’Reilly, C., Nielsen, T.: Assessing EEG sleep spindle propagation. Part 2: experimental characterization. J. Neurosci. Methods 221, 215–227 (2014)
Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Koutras, A., Kostopoulos, G.K. (2018). EEG-Based Person Identification Using Rhythmic Brain Activity During Sleep. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-01424-7_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01423-0
Online ISBN: 978-3-030-01424-7
eBook Packages: Computer ScienceComputer Science (R0)