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Subject Identification from Low-Density EEG-Recordings of Resting-States: A Study of Feature Extraction and Classification

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Advances in Information and Communication (FICC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 70))

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Abstract

A new concept of low-density electroencephalograms-based (EEG) Subject identification is proposed in this paper. To that aim, EEG recordings of resting-states were analyzed with 3 different classifiers (SVM, k-NN, and naive Bayes) using Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) for feature extraction and their accuracies were estimated to compare their performances. To explore the feasibility of using fewer channels with minimum loss of accuracy, the methods were applied to a dataset of 27 Subjects (From 5 sessions of 30 instances per Subject) recorded using the EMOTIV EPOC device with 1 set of 14 channels and 4 subsets (8, 4, 2 and 1 channel) that were selected using a greedy algorithm. The experiments were reproduced using fewer instances each time to observe the evolution of the accuracy using both; fewer channels and fewer instances. The results of this experiments suggest that EMD compared with DWT is a more robust technique for feature extraction from brain signals to identify Subjects during resting-states, particularly when the amount of information is reduced: e.g., using Linear SVM and 30 instances per Subject, the accuracies obtained using 14 channels were 0.91 and 0.95, with 8 channels were 0.87 and 0.89 with EMD and DWT repectively but were reversed in favor of EMD when the number of channels was reduced to 4 channels (0.76 and 0.74), 2 (0.64 and 0.56) and 1 channel (0.46 and 0.31). The general observed trend is that, Linear SVM exhibits higher accuracy rates using high-density EEG (0.91 with 14 channels) while Gaussian naive Bayes exhibits better accuracies when using low-density EEG in comparison with the other classifiers (with EMD 0.88, 0.81, 0.76 and 0.61 respectively for 8, 4, 2 and 1 channel). The findings of these experiments reveal an important insight for continuing the exploration of low-density EEG for Subject identification.

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References

  1. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14(1), 4–20 (2004)

    Article  Google Scholar 

  2. Jain, A.K., Ross, A., Uludag, U.: Biometric template security: challenges and solutions. In: Signal Processing Conference 13th European, pp. 1–4. IEEE (2005)

    Google Scholar 

  3. Valizadeh, S.A., Liem, F., Mérillat, S., Hänggi, J., Jäncke, L.: Identification of individual subjects on the basis of their brain anatomical features. Sci. Rep. 8(1), 5611 (2018)

    Article  Google Scholar 

  4. Moctezuma, L.A., Molinas, M.: EEG-based subjects identification based on biometrics of imagined speech using EMD. In: Submitted to The 11th International Conference on Brain Informatics (BI 2018) (2018)

    Chapter  Google Scholar 

  5. Moctezuma, L.A., Molinas, M., García, A.A.T., Pineda, L.V., Carrillo, M.: Towards an API for EEG-based imagined speech classification. In: International Conference on Time Series and Forecasting (2018)

    Google Scholar 

  6. Moctezuma, L.A.: Distinción de estados de actividad e inactividad lingüística para interfaces cerebro computadora. Thesis project of Master Degree (2017)

    Google Scholar 

  7. Nishimoto, T., Azuma, Y., Morioka, H., Ishii, S.: Individual identification by resting-state EEG using common dictionary learning. In: International Conference on Artificial Neural Networks, pp. 199–207. Springer, Cham (2017)

    Chapter  Google Scholar 

  8. Ashby, C., Bhatia, A., Tenore, F., Vogelstein, J.: Low-cost electroencephalogram (EEG) based authentication. In: 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 442–445 (2011)

    Google Scholar 

  9. Palaniappan, R.: Electroencephalogram signals from imagined activities: a novel biometric identifier for a small population. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 604–611. Springer, Berlin, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Jayarathne, I., Cohen, M., Amarakeerthi, S.: BrainID: development of an EEG-based biometric authentication system. In: 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1–6 (2016)

    Google Scholar 

  11. Jayarathne, I., Cohen, M., Amarakeerthi, S.: Survey of EEG-based biometric authentication. In: 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), pp. 324–329 (2017)

    Google Scholar 

  12. Del Pozo-Banos, M., Alonso, J.B., Ticay-Rivas, J.R., Travieso, C.M.: Electroencephalogram subject identification: a review. Expert Syst. Appl. 41(15), 6537–6554 (2014)

    Article  Google Scholar 

  13. Elman, L.B., McCluskey, L.: Clinical features of amyotrophic lateral sclerosis and other forms of motor neuron disease. Up-to-date, p. 23. Wolters Kluwer Health, Waltham (2012)

    Google Scholar 

  14. Feller, T.G., Jones, R.E., Netsky, M.G.: Amyotrophic lateral sclerosis and sensory changes. Virginia Med. Mon. 93(6), 328 (1966)

    Google Scholar 

  15. Ma, L., Minett, J.W., Blu, T., Wang, W.S.: Resting state EEG-based biometrics for individual identification using convolutional neural networks. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2848–2851 (2015)

    Google Scholar 

  16. Molinas, M., Van der Meer, A., Skjærvold, N.K., Lundheim, L.: David versus Goliath: single-channel EEG unravels its power through adaptive signal analysis - FlexEEG. Research project (2018)

    Google Scholar 

  17. Xiong, J., Ma, L., Wang, B., Narayana, S., Eugene, E.P., Egan, G.F., Fox, P.T.: Long-term motor training induced changes in regional cerebral blood flow in both task and resting states. Neuroimage 45(1), 75–82 (2009)

    Article  Google Scholar 

  18. Golanov, E.V., Yamamoto, S., Reis, D.J.: Spontaneous waves of cerebral blood flow associated with a pattern of electrocortical activity. Am. J. Physiol. Regul. Integr. Comp. Physiol. 266(1), R204–R214 (1994)

    Article  Google Scholar 

  19. Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M.: Electrophysiological signatures of resting state networks in the human brain. Proc. Nat. Acad. Sci. 104(32), 13170–13175 (2007)

    Article  Google Scholar 

  20. Jasper, H.: Report of the committee on methods of clinical examination in electroencephalography. Electroencephalogr. Clin. Neurophysiol. 10, 370–375 (1958)

    Article  Google Scholar 

  21. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to algorithms. In: Greedy Algorithms. MIT press, Cambridge (2001)

    Google Scholar 

  22. Torres-García, A.A., Reyes-García, C.A., Villaseñor-Pineda, L., Ramírez-Cortís, J.M.: Análisis de señales electroencefalográficas para la clasificacin de habla imaginada. Revista mexicana de ingeniería biomédica 34(1), 23–39 (2013)

    Google Scholar 

  23. Boutana, D., Benidir, M., Barkat, B.: On the selection of intrinsic mode function in EMD method: application on heart sound signal. In: 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), pp. 1–5 (2010)

    Google Scholar 

  24. Rish, I., Hellerstein, J., Thathachar, J.: An analysis of data characteristics that affect naive Bayes performance. IBM TJ Watson Research Center 30, (2001)

    Google Scholar 

  25. Averbuch, A.Z., Zheludev, V.A.: Construction of biorthogonal discrete wavelet transforms using interpolatory splines. Appl. Comput. Harmonic Anal. 12(1), 25–56 (2002)

    Article  MathSciNet  Google Scholar 

  26. Gao, Y., Ge, G., Sheng, Z., Sang, E.: Analysis and solution to the mode mixing phenomenon in EMD. In: Congress on Image and Signal Processing, CISP’08, vol. 5, pp. 223–227 (2008)

    Google Scholar 

  27. Fosso, O.B., Molinas. M.: Method for Mode Mixing Separation in Empirical Mode Decomposition. arXiv preprint arXiv:1709.05547 (2017)

  28. Wang, Y.-H., Yeh, C.-H., Young, H.-W.V., Hu, K., Lo, M.-T.: On the computational complexity of the empirical mode decomposition algorithm. Phys. A Stat. Mech. Appl. 400, 159–167 (2014)

    Article  Google Scholar 

  29. Fontugne, R., Borgnat, P., Flandrin, P.: Online empirical mode decomposition. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4306–4310 (2017)

    Google Scholar 

  30. Faltermeier, R., Zeiler, A., Keck, I.R., Tom, A.M., Brawanski, A., Lang, E.W.: Sliding empirical mode decomposition. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2010)

    Google Scholar 

  31. Mahmudova, S.: Analysis of biometric authentication methods of users in clouds. Int. J. Adv. Eng. Technol. 1(5), 14–17 (2017)

    Google Scholar 

  32. Rehman, N., Mandic, D.P.: Multivariate empirical mode decomposition. Proc. R. Soc. Lond. A 466(2117), 1291–1302 (2010)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by Enabling Technologies - NTNU, under the project “David versus Goliath: single-channel EEG unravels its power through adaptive signal analysis - FlexEEG”.

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Authors and Affiliations

  1. Department of Engineering Cybernetics, Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Luis Alfredo Moctezuma & Marta Molinas

Authors
  1. Luis Alfredo Moctezuma
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  2. Marta Molinas
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Correspondence to Luis Alfredo Moctezuma .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Moctezuma, L.A., Molinas, M. (2020). Subject Identification from Low-Density EEG-Recordings of Resting-States: A Study of Feature Extraction and Classification. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_57

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