Advertisement

Abstract

This paper presents the study we have done to detect “meditation” brain state by analyzing electroencephalographic (EEG) data. We firstly discuss what is “meditation” state and some prior studies on meditation. We then discuss how meditation state can be reflected in the subject’s brain waves; and what features of the brain waves data can be used in machine learning algorithms to classify meditation state from other states. We studied the suitability of 3 types of entropy: Shannon entropy, approximate entropy, and sample entropy in different circumstances. We found that overall Sample entropy is a good tool to extract information from EEG data. Discretization of EEG data enhances the classification rates by using both the approximate entropy and Shannon entropy.

Keywords

Meditation Machine learning Electroencephalogram (EEG) Entropy Classification 

References

  1. 1.
    Kai, Z., Ma, R.: Translation and Annotations of <<Essentials of Meditation≫. In: Wang, M. (ed.) Series of Chinese Secret Archives of Life Caring. Beijing Science and Technology Press, Beijing (1995)Google Scholar
  2. 2.
    Cella, D.F., Tulsky, D.S., Gray, G., et al.: The functional assessment of cancer therapy scale: development and validation of the general measure. J. Clin. Oncol. 11, 570–579 (1993)CrossRefGoogle Scholar
  3. 3.
    McNair, D., Loor, M., Droppleman, L.: Profile of mood status (revised). EdITS/Educational and Industrial Testing Services, San Diego (1992)Google Scholar
  4. 4.
    Mruk, C.J., Hartzell, J.: Zen & Psychotherapy: Integrating Traditional and Nontraditional Approaches. Springer Publishing Company, New York (2003)Google Scholar
  5. 5.
    Kropotov, J.: Quantitative EEG, Event-Related Potentials and Neurotherapy, p. 2009. Elsevier Inc., Amsterdam (2009). 525 B Street, Suite 1900, San Diego, CA 92101-4495, USAGoogle Scholar
  6. 6.
    Larsen, E.: Classification of EEG Signals in a Brain-Computer Interface System. Norwegian University of Science and Technology, Norway, PhD thesis (2011)Google Scholar
  7. 7.
    Sławińska, U., Kasicki, S.: The frequency of rat’s hippocampal theta rhythm is related to the speed of locomotion. Brain Res. 796(1), 327–331 (1998)Google Scholar
  8. 8.
    Yang, R., Song, A., Xu, B.: Feature extraction of motor imagery EEG based on wavelet transform and higher-order statistics. Int. J. Wavelets Multiresolut. Inf. Process. 8(3), 373–384 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Zhuang, T., Zhao, H., Tang, Z.: A study of brainwave entrainment based on EEG brain dynamics. Comput. Inf. Sci. 2(2), 81–86 (2009)Google Scholar
  10. 10.
    Yuvaraj, R., Murugappan, M., Ibrahim, N., Sundaraj, K., Omar, M., Mohamad, K., Palaniappan, R.: Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson’s disease. Int. J. Psychophysiol. 94(3), 482–495 (2014)CrossRefGoogle Scholar
  11. 11.
    Direito, B., Teixeira, C., Ribeiro, B., Branco, M., Sales, F., Dourado, A.: Modeling epileptic brain states using EEG spectral analysis and topographic mapping. J. Neurosci. Methods 210(2), 220–229 (2012)CrossRefGoogle Scholar
  12. 12.
    Lin, H.: Measurable meditation. In: Proceedings of the International Symposium on Science 2.0 and Expansion of Science (S2ES 2010), The 14th World Multiconference on Systemics, Cybernetics and Informatics (WMSCI 2010), Orlando, Florida, 29 June–2 July 2010, pp. 56–61 (2010)Google Scholar
  13. 13.
    Loizzo, J.J., Peterson, J.C., Charlson, M.E., Wolf, E.J., Altemus, M., Briggs, W.M., Vahdat, L.T., Caputo, T.A.: The effect of a contemplative self-healing program on quality of life in women with breast and gynecologic cancers. Altern. Ther. Health Med. 16(3), 30–37 (2010)Google Scholar
  14. 14.
    Lengacher, C.A., Johnson-Mallard, V., Post-White, J., Moscoso, M.S., Jacobsen, P.B., Klein, T.W., Widen, R.H., Fitzgerald, S.G., Shelton, M.M., Barta, M., Goodman, M., Cox, C.E., Kip, K.E.: Randomized controlled trial of mindfulness-based stress reduction (MBSR) for survivors of breast cancer. Psychology 18(12), 1261–1272 (2009)Google Scholar
  15. 15.
    Oh, B., Butow, P., Mullan, B., Clarke, S.: Medical Qigong for cancer patients: pilot study of impact on quality of life, side effects of treatment and inflammation. Am. J. Chin. Med. 36(3), 459–472 (2008)CrossRefGoogle Scholar
  16. 16.
    Hölzel, B.K., Ott, U., Hempel, H., Hackl, A., Wolf, K., Stark, R., Vaitl, D.: Differential engagement of anterior cingulate and adjacent medial frontal cortex in adept meditators and nonmeditators. Neurosci. Lett. 421(1), 16–21 (2007)CrossRefGoogle Scholar
  17. 17.
    Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Rev. Neurosci. 8(9), 700–711 (2007)CrossRefGoogle Scholar
  18. 18.
    Sun, S., Zhang, C., Zhang, D.: An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recogn. Lett. 28(15), 2157–2163 (2007)CrossRefGoogle Scholar
  19. 19.
    Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., Dickhaus, H.: 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 (2012)CrossRefGoogle Scholar
  20. 20.
    Guler, L., Beyli, E.D.U.: Multiclass support vector machines for eeg-signals classification. IEEE Trans. Inf Technol. Biomed. 11(2), 117–126 (2007)CrossRefGoogle Scholar
  21. 21.
    Song, Y., Lio, P., et al.: A new approach for epileptic seizure detection: sample entropy based feature extraction and extreme learning machine. J. Biomed. Sci. Eng. 3(06), 556 (2010)CrossRefGoogle Scholar
  22. 22.
    Lebowitz, J., Lewis, M.S., Schuck, P.: Modern analytical ultracentrifugation in protein science: a tutorial review. Protein Sci. 11(9), 2067–2079 (2002)CrossRefGoogle Scholar
  23. 23.
    Johnson, M.L., Brand, L.: Numerical Computer Methods, Part E, vol. 384. Academic Press, Cambridge (2004)Google Scholar
  24. 24.
    Prasad, A.M., Iverson, L.R., Liaw, A.: Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2), 181–199 (2006)CrossRefGoogle Scholar
  25. 25.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefzbMATHGoogle Scholar
  26. 26.
    Xuan, G., Zhang, W., Chai, P.: EM algorithms of Gaussian mixture model and hidden Markov model. In: 2001 Proceedings of the International Conference on Image Processing, vol. 1, pp. 145–148. IEEE (2001)Google Scholar
  27. 27.
    Li, Y., Chang, Y., Lin, H.: Statistical machine learning in brain state classification using EEG data. Open J. Big Data (OJBD) 1(2), 19–33 (2015). RonPub UG (haftungsbeschränkt), Lübeck, GermanyGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering TechnologyUniversity of Houston-DowntownHoustonUSA
  2. 2.Department of MathematicsIllinois State UniversityNormalUSA

Personalised recommendations