Age and gender classification using brain–computer interface

Abstract

With the development of Internet of things (IOT), it is now possible to connect various heterogeneous devices together using Internet. The devices are able to share their information for various applications including health care, security and monitoring. IOT facilitates patients to self-monitor their physiological states invariably and doctors to monitor their patients remotely. Electroencephalography (EEG) provides a monitoring method to record such electrical activities of the brain using sensors. In this paper, we present an automatic age and gender prediction framework of users based on their neurosignals captured during eyes closed resting state using EEG sensor. Using EEG sensor, brain activities of 60 individuals with different age groups varying between 6 and 55 years and gender (i.e., male and female) have been recorded using a wireless EEG sensor. Discrete wavelet transform frequency decomposition has been performed for feature extraction. Next, random forest classifier has been applied for modeling the brain signals. Lastly, the accuracies have been compared with support vector machine and artificial neural network classifiers. The performance of the system has been tested using user-independent approach with an accuracy of 88.33 and 96.66% in age and gender prediction, respectively. It has been analyzed that oscillations in beta and theta band waves show maximum age prediction, whereas delta rhythm leads to highest gender classification rates. The proposed method can be extended to different IOT applications in healthcare sector where age and gender information can be automatically transmitted to hospitals and clinics through Internet.

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Notes

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    https://sites.google.com/site/kaurbarjinder/.

References

  1. 1.

    Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

  2. 2.

    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gen Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  3. 3.

    Chen S-L, Lee H-Y, Chen C-A, Huang H-Y, Luo C-H (2009) Wireless body sensor network with adaptive low-power design for biometrics and healthcare applications. IEEE Syst J 3(4):398–409

    Article  Google Scholar 

  4. 4.

    Gauba H, Kumar P, Roy PP, Singh P, Dogra DP, Raman B (2017) Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Netw 92:77–88

    Article  Google Scholar 

  5. 5.

    Ramirez R, Vamvakousis Z (2012) Detecting emotion from EEG signals using the emotive EPOC device. In: International conference on brain informatics. Springer, pp 175–184

  6. 6.

    Kumar P, Saini R, Roy PP, Dogra DP (2017) A bio-signal based framework to secure mobile devices. J Netw Comput Appl 89:62–71

    Article  Google Scholar 

  7. 7.

    Marcel S, Millán JR (2007) Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Trans Pattern Anal Mach Intell 29(4):743–752

    Article  Google Scholar 

  8. 8.

    Wang L, Zhang X, Zhong X, Zhang Y (2013) Analysis and classification of speech imagery EEG for BCI. Biomed Signal Process Control 8(6):901–908

    Article  Google Scholar 

  9. 9.

    Liao L-D, Chen C-Y, Wang I-J, Chen S-F, Li S-Y, Chen B-W, Chang J-Y, Lin C-T (2012) Gaming control using a wearable and wireless EEG-based brain–computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil 9(1):1

    Article  Google Scholar 

  10. 10.

    Dawson D, Searle AK, Paterson JL (2014) Look before you (s) leep: evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry. Sleep Med Rev 18(2):141–52

    Article  Google Scholar 

  11. 11.

    Nguyen P, Tran D, Huang X, Ma W (2013) Age and gender classification using EEG paralinguistic features. In: 6th International conference on neural engineering, pp 1295–1298

  12. 12.

    Clarke AR, Barry RJ, McCarthy R, Selikowitz M (2001) Age and sex effects in the EEG: development of the normal child. Clin Neurophysiol 112(5):806–814

    Article  Google Scholar 

  13. 13.

    Barry RJ, Clarke AR, McCarthy R, Selikowitz M, Johnstone SJ, Rushby JA (2004) Age and gender effects in EEG coherence: I. Developmental trends in normal children. Clin Neurophysiol 115(10):2252–2258

    Article  Google Scholar 

  14. 14.

    Jaušovec N, Jaušovec K (2005) Sex differences in brain activity related to general and emotional intelligence. Brain Cogn 59(3):277–286

    Article  Google Scholar 

  15. 15.

    Langrova J, Kremlacek J, Kuba M, Kubova Z, Szanyi J (2012) Gender impact on electrophysiological activity of the brain. Physiol Res 61:S119

    Google Scholar 

  16. 16.

    Paiva LRM, Pereira AA, Almeida MFS, Cavalheiro GL, Milagre ST, Andrade AO (2012) Analysis of the relationship between EEG signal and aging through linear discriminant analysis. Revista Brasileira de Engenharia Biomédica 28(2):155–168

    Google Scholar 

  17. 17.

    Nguyen P, Tran D, Vo T, Huang X, Ma W, Phung D (2013) EEG-based age and gender recognition using tensor decomposition and speech features. In: International conference on neural information processing, pp 632–639

  18. 18.

    Franke K, Ziegler G, Klöppel S, Gaser C, Initiative ADN et al (2010) Estimating the age of healthy subjects from t 1-weighted MRI scans using kernel methods: Exploring the influence of various parameters. Neuroimage 50(3):883–892

    Article  Google Scholar 

  19. 19.

    Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. In: Conference on computer vision and pattern recognition workshops, pp 34–42

  20. 20.

    Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl Based Syst 88:85–96

    Article  Google Scholar 

  21. 21.

    Sharma R, Pachori RB, Acharya UR (2015) Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2):669–691

    Article  Google Scholar 

  22. 22.

    Faust O, Acharya UR, Adeli H, Adeli A (2015) Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure Eur J Epilepsy 26:56–64

    Article  Google Scholar 

  23. 23.

    Acharya UR, Molinari F, Sree SV, Chattopadhyay S, Ng K-H, Suri JS (2012) Automated diagnosis of epileptic EEG using entropies. Biomed Signal Process Control 7(4):401–408

    Article  Google Scholar 

  24. 24.

    Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  25. 25.

    Fraiwan L, Lweesy K, Khasawneh N, Wenz H, Dickhaus H (2012) Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput Methods Programs Biomed 108(1):10–19

    Article  Google Scholar 

  26. 26.

    Donos C, Dümpelmann M, Schulze-Bonhage A (2015) Early seizure detection algorithm based on intracranial EEG and random forest classification. Int J Neural Syst 25(05):1550023

    Article  Google Scholar 

  27. 27.

    Badcock NA, Mousikou P, Mahajan Y, de Lissa P, Thie J, McArthur G (2013) Validation of the Emotiv EPOC® EEG gaming system for measuring research quality auditory ERPs. PeerJ 1:e38

    Article  Google Scholar 

  28. 28.

    Hargittai S (2005) Savitzky-Golay least-squares polynomial filters in ECG signal processing. In: Computers in cardiology, pp 763–766

  29. 29.

    Rahman FA, Othman M (2015) Real time eye blink artifacts removal in electroencephalogram using Savitzky-Golay referenced adaptive filtering. In: International conference for innovation in biomedical engineering and life sciences, pp 68–71

  30. 30.

    Wali MK, Murugappan M, Ahmmad RB (2012) Mathematical implementation of hybrid fast Fourier transform and discrete wavelet transform for developing graphical user interface using visual basic for signal processing applications. J Mech Med Biol 12(05):1240031

    Article  Google Scholar 

  31. 31.

    Tatum WO IV (2014) Handbook of EEG interpretation. Demos Medical Publishing, New York

    Book  Google Scholar 

  32. 32.

    Soleymani M, Asghari-Esfeden S, Fu Y, Pantic M (2016) Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Trans Affect Comput 7(1):17–28

    Article  Google Scholar 

  33. 33.

    Kaur B, Singh D, Roy PP (2017) A novel framework of EEG-based user identification by analyzing music-listening behavior. Multimed Tools Appl 76(24):25581–25602

    Article  Google Scholar 

  34. 34.

    Vapnik V (2013) The nature of statistical learning theory. Springer, New York

    MATH  Google Scholar 

  35. 35.

    Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C et al (2001) Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7(6):673–679

    Article  Google Scholar 

  36. 36.

    Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J (2013) Artificial neural networks in medical diagnosis. J Appl Biomed 11(2):47–58

    Article  Google Scholar 

  37. 37.

    Bajaj V, Pachori RB (2012) Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 16(6):1135–1142

    Article  Google Scholar 

  38. 38.

    Übeyli ED (2009) Combined neural network model employing wavelet coefficients for EEG signals classification. Digit Signal Proc 19(2):297–308

    Article  Google Scholar 

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Correspondence to Barjinder Kaur.

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Kaur, B., Singh, D. & Roy, P.P. Age and gender classification using brain–computer interface. Neural Comput & Applic 31, 5887–5900 (2019). https://doi.org/10.1007/s00521-018-3397-1

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Keywords

  • Electroencephalography (EEG) sensors
  • Internet of things (IOT)
  • Healthcare
  • Random forest (RF)