An innovative and robust technique for human identification and authentication based on a secure clinical signals transmission

Document Type : Research Paper

Authors

1 College of Dentistry, University of Al-Ameed, Karbala PO Box 198, Iraq

2 College of Pharmacy, University of Al-Ameed, Karbala PO Box 198, Iraq

3 Department of Computer Engineering and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran

Abstract

EEG (Electroencephalogram) is brain waves measure. It is available test allowed to discover the brain functions over time. The brain troubles are evaluated by EEG. It is used to locate the activity in the brain during a seizure and to consider the patients who suffer from brain functionality problems. These troubles include tumors, coma, confusion and long-term difficulties (such as weakness associated with a stroke). The acquisition of EEG signals requires contact and liveliness and these signals are changes under stress that make so potentially unnecessary if it is acquired under menace. In this paper, an innovative and robust solution for this problem is introduced. To this end, the manner depends on models of various data compression models of information-theoretic plus the metrics symmetry related to Kolmogorov complexity. The proposed procedure compares two EEG segments and clusters the data into three groups: a corresponding record for each participant, a distinct person for each group, and self-participant. The technique was used to determine the database participant based on EEG signals. Using a distance measuring approach suggested in this scheme, a 1-NN classifier was constructed. Nearly every person in the underlying database could be accurately identified by the classifier with $96\%$ accuracy

Keywords

[1] A.S. Abdulbaqi, A.J. Obaid and S.A. Hmeed Alazawi, A smart system for health caregiver based on IoMT: toward
tele-health caregiving, Int. J. Online Biomed. Engin. 17(7) (2021) 70–87.
[2] A.Sh. Abdulbaqi, A.J. Obaid and A.H. Mohammed, ECG signals recruitment to implement a new technique for
medical image encryption, J. Discrete Math. Sci. Cryptog. (2021) 1–11.
[3] A.S. Abdulbaqi and I.Y. Panessai, Designing and implementation of a biomedical module for vital signals measurements based on embedded system, Int. J. Adv. Sci. Technol. 29(3) (2020) 3866–3877.
[4] M. Abo-Zahhad, S.M. Ahmed and S.N. Abbas, State-of-the-art methods and future perspectives for personal
recognition based on electroencephalogram signals, IET Biom. 4(3) (2015) 179–90.
[5] B.C. Armstrong, M.V. Ruiz-Blondet, N. Khalifian, K.J. Kurtz, Z. Jin and S. Laszlo, Brain print: assessing the
uniqueness, collectability, and permanence of a novel method for ERP biometrics, Neurocomp. 166 (2015) 59–67.
[6] K. Brigham and B.V.K.V. Kumar, Subject identification from electroencephalogram (EEG) signals during imagined speech, 2010 Fourth IEEE Int. Conf. Biomet. Theory, Appl. Syst. (2010) 1–8.[7] A. Buda and A. Jarynowski, Life-Time of Correlations and its Applications, Andrzej Buda and Wydawnictwo
Niezaleňôzne, 2010.
[8] P.T. Dao, X.J. Li and H.N. Do, Lossy compression techniques for EEG signals, 2015 Int. Conf. Adv. Tech.
Communic. (2015) 154–159.
[9] D. Del Testa and M. Rossi, Lightweight Lossy compression of biometric patterns via denoising auto encoders,
IEEE Signal Proc. Lett. 22(12) (2015) 2304–2308.
[10] S. Fauvel and R.K. Ward, An energy efficient compressed sensing framework for the compression of electroencephalogram signals, Sensors 14(1) (2014) 1474–1496.
[11] H.U. Jian-feng, Multi feature biometric system based on EEG signals, Proc. 2nd Int. Conf. Interaction Sci. (2009)
1341–1345.
[12] M. Li and P. Vitanyi, An Introduction to Kolmogorov Complexity and Its Applications, Springer Science &
Business Media(SSBM), 2009.
[13] A. Shukla and A. Majumdar, Row-sparse blind compressed sensing for reconstructing multichannel EEG signals,
Biomed. Signal Proc. Cont. 18 (2015) 174–178.
[14] K. Srinivasan, J. Dauwels and M.R. Reddy, A two-dimensional approach for lossless EEG compression, Biomed.
Signal Proc. Cont. 6(4) (2011) 387–394.
[15] N. Sriraam, Quality-on-demand compression of EEG signals for telemedicine applications using neural network
predictors, Int. J. Telemed. Appl. (2011) 860549.
[16] N. Sriraam, A high-performance lossless compression scheme for EEG signals using wavelet transform and neural
network predictors, Int. J. Telemed. Appl. (2012).
Volume 13, Issue 1
March 2022
Pages 603-613
  • Receive Date: 24 June 2021
  • Revise Date: 12 August 2021
  • Accept Date: 06 September 2021
  • First Publish Date: 24 September 2021