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A new hybrid approach for feature extraction and selection of electroencephalogram signals in case of person recognition

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Abstract

Electroencephalogram (EEG) signals are possible biomarkers for person recognition. Biometric systems use an interdisciplinary approach for improving the accuracy of person identification. This paper describes different feature extraction schemes for the classification of EEG signals. Autoregressive model features come out best performing features among all. After feature extraction, feature selection techniques are applied to improve classification accuracy by reducing the dimensions of data, which help in reducing computation cost and chances of overfitting of data. Principal component analysis (PCA) and linear discriminant analysis (LDA) are used for dimensionality reduction. Furthermore, different machine learning algorithms are used for the classification of EEG signals. The outcomes of this study indicate that the higher classification accuracy of 99.80% is achieved with LDA as a feature selection and K-nearest-neighbour as a classifier. The findings of the study demonstrate that the proposed combination of feature extraction, feature selection, and classification technique has the potential to classify the EEG signals for person identification.

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Availability of data and materials

Online available dataset published by UCI KDD (Hettich, S. and Bay 1999) is used in current study.

References

  1. Kaliraman B, Vig R (2016) Iris recognition using hybrid transform. Commun Comput Syst. https://doi.org/10.1201/9781315364094

    Article  Google Scholar 

  2. Poulos M, Rangoussi M, Chrissikopoulos V, Evangelou A (1999) Person identification based on parametric processing of the EEG. In: Electronics, circuits and systems. Proceedings of ICECS, vol 1, no 1, pp 283–286, 1999. https://doi.org/10.1109/ICECS.1999.813403

  3. Kaliraman B, Singh P, Duhan M (2020) Use of EEG as a unique human biometric trait for authentication of an individual. In: Advances in communication and computational technology. pp 277–286. https://doi.org/10.1007/978-981-15-5341-7_5

  4. Bhawna K, Priyanka, Duhan M (2020) Electroencephalogram based biometric system: a review. In: Advances in communication and computational technology. pp 57–77. https://doi.org/10.1007/978-981-15-5341-7_23

  5. Coronato A, Cuzzocrea A (2020) An innovative risk assessment methodology for medical information systems. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.3023553

    Article  Google Scholar 

  6. Nakra A, Duhan M (2020) Feature extraction and dimensionality reduction techniques with their advantages and disadvantages for EEG-Based BCI system: a review. IUP J Comput Sci 14(1):21–34

    Google Scholar 

  7. Nishimoto T, Higashi H, Morioka H, Ishii S (2020) EEG-Based Personal identification method using unsupervised feature extraction and its robustness against intra-subject variability. J Neural Eng 17(2):026007. https://doi.org/10.1088/1741-2552/ab6d89

    Article  Google Scholar 

  8. Abed SS, Abed ZF (2020) User authentication system based specified brain waves. J Discrete Math Sci Cryptogr 23(5):1021–1024. https://doi.org/10.1080/09720529.2019.1700920

    Article  Google Scholar 

  9. Chawla M, Duhan M (2014) Applications of recent metaheuristics optimisation algorithms in biomedical engineering: a review. Int J Biomed Eng Technol 16(3):268–278. https://doi.org/10.1504/IJBET.2014.065807

    Article  Google Scholar 

  10. Kaewwit C, Lursinsap C, Sophatsathit P (2017) High accuracy EEG biometrics identification using ICA and AR model. J ICT 2(2):354–373

    Google Scholar 

  11. Thakur S, Dharavath R, Edla DR (2020) Spark and rule-KNN based scalable machine learning framework for EEG deceit identification. Biomed Signal Process Control 58:101886. https://doi.org/10.1016/j.bspc.2020.101886

    Article  Google Scholar 

  12. Roushdy M, Salem AM, Abdulrahman SA (2019) Human Identification based on electroencephalography signals using sample entropy and horizontal visibility graphs. WSEAS Trans Signal Process 15, e-issn: 2224-3488.

  13. Ullah H, Aamir A, Malik S, Fayyaz R (2015) Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Australas Phys Eng Sci Med. https://doi.org/10.1007/s13246-015-0333-x

    Article  Google Scholar 

  14. Yazdani A, Roodaki A, Rezatofighi SH, Misaghian K, Setarehdan SK (2008) Fisher linear discriminant based person identification using visual evoked potentials. In: 9th international conference on signal processing, 2008. https://doi.org/10.1109/ICOSP.2008.4697459

  15. Delpozo-banos M, Travieso CM, Weidemann CT, Alonso JB (2015) EEG biometric identification : a thorough exploration of the time-frequency domain. J Neural Eng 12:056019. https://doi.org/10.1088/1741-2560/12/5/056019

    Article  Google Scholar 

  16. Hettich SD, Bay S (1999) The UCI KDD archive. http://kdd.ics.uci.edu. Accessed 17 June 2020

  17. Snodgrass JG, Vanderwart M (1980) A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. J Exp Psychol Hum Learn Mem 6(2):174–215. https://doi.org/10.1037/0278-7393.6.2.174

    Article  Google Scholar 

  18. Hjorth B (1970) EEG analysis based on time domain properties. Electroencephalogr Clin Neurophysiol 29(3):306–310. https://doi.org/10.1016/0013-4694(70)90143-4

    Article  Google Scholar 

  19. Akansu AN, Haddad RA (2001) Wavelet transform. Multiresolution signal decomposition. Elsevier, pp 391–442

    Chapter  Google Scholar 

  20. Heideman M, Johnson D, Burrus CS (1984) Gauss and the History of the fast Fourier transform. IEEE Signal Process Mag 1(3):14–21

    MATH  Google Scholar 

  21. Contributors W (2020) Autoregressive model. Wikipedia, the free encyclopedia. https://en.wikipedia.org/w/index.php?title=Autoregressive_model&oldid=950903028. Accessed 03 Jul 2020

  22. McLachlan GJ (2004) Discriminant Analysis And Statistical Pattern Recognition. Wiley

    MATH  Google Scholar 

  23. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794, 2016. https://doi.org/10.1145/2939672.2939785

  24. Ho TK (1995) Random decision forests. In: Proceedings of the international conference on document analysis and recognition, ICDAR, vol 1, pp 278–282, 1995. https://doi.org/10.1109/ICDAR.1995.598994

  25. Parekh M CNN. https://medium.com/nybles/a-brief-guide-to-convolutional-neural-network-cnn-642f47e88ed4

  26. Das R, Maiorana E, Campisi P (2017) Visually evoked potential for EEG biometrics using convolutional neural network. In: 25th European signal processing conference, EUSIPCO 2017, vol 2017-January, pp 951–955, 2017. https://doi.org/10.23919/EUSIPCO.2017.8081348

  27. Xu G et al (2019) A deep transfer convolutional neural network framework for EEG signal classification. IEEE Access 7:112767–112776. https://doi.org/10.1109/ACCESS.2019.2930958

    Article  Google Scholar 

  28. Ozdenizci O, Wang Y, Koike-Akino T, Erdogmus D (2019) Adversarial deep learning in EEG biometrics. IEEE Signal Process Lett 26(5):710–714. https://doi.org/10.1109/LSP.2019.2906826

    Article  Google Scholar 

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Acknowledgements

I would like to give a sincere thanks to my Ph.D. supervisor, Dr. Manoj Duhan, for his constant guidance as well as providing necessary information and for reviewing various drafts of this paper.

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  1. Department of ECE, DCRUST, Sonipat, India

    Bhawna Kaliraman & Manoj Duhan

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  1. Bhawna Kaliraman
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  2. Manoj Duhan
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Correspondence to Bhawna Kaliraman.

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Kaliraman, B., Duhan, M. A new hybrid approach for feature extraction and selection of electroencephalogram signals in case of person recognition. J Reliable Intell Environ 7, 241–251 (2021). https://doi.org/10.1007/s40860-021-00148-z

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  • DOI: https://doi.org/10.1007/s40860-021-00148-z

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