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Analyzing Effect of Meditation Using Higher Order Crossings and Functional Connectivity

  • Shruti Phutke
  • Narendra Jadhav
  • Ramchandra Manthalkar
  • Yashwant Joshi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

People are experiencing difficulties in adapting to the rapid changes in work and social fabric due to the evolution of advanced technologies in everyday life. Health and well-being of an individual in the existing world is important for proper living. Meditation improves the adaptability of an individual to live a healthy and social life. To verify this, an experiment is designed with the simple meditation practice called Focused Attention for 8 weeks. The brain activity is recorded of 11 subjects using EMOTIV EPOC+ EEG device before (pre-meditation) and after (post-meditation) meditation. Features called Higher Order Crossings and Functional Connectivity are used to analyze the effect of meditation. The results indicated a decrease in HOC values for frontal, parietal, and occipital lobes and increase in HOC of temporal lobe. The interhemispheric connectivity increased after meditation practice.

Keywords

Meditation EEG Higher order crossings Functional connectivity 

Notes

Declaration

The work reported in this chapter is approved by the ethical approval committee of SGGSIE&T, Nanded. The committee consist of Dr. Mrs. S. S. Shinde (Chairperson), Dr. S. T. Hamde, Dr. R. R. Manthalkar, Prof. A. K. Dhaolwe, Prof. A. K. Dhadve.

References

  1. 1.
    Davidson, R.J., Lutz, A.: Buddha’s brain: neuroplasticity and meditation [in the spotlight]. IEEE Signal Process. Mag. 25(1), 176–174 (2008)CrossRefGoogle Scholar
  2. 2.
    Nidal, K., Malik, A.S.: EEG/ERP Analysis: Methods and Applications. Crc Press (2014)Google Scholar
  3. 3.
    Lo, P.-C., Zhu, Q.: Microstate analysis of alpha-event brain topography during Chan meditation. In: 2009 International Conference on Machine Learning and Cybernetics. IEEE (2009)Google Scholar
  4. 4.
    Chang, K.-M., Lo, P.-C.: F-VEP and alpha-suppressed EEG-physiological evidence of inner-light perception during Zen meditation. Biomed. Eng. Appl. Basis Commun. 18(01), 1–7 (2006)CrossRefGoogle Scholar
  5. 5.
    Sobolewski, A., et al.: Impact of meditation on emotional processing—a visual ERP study. Neurosci. Res. 71(1), 44–48 (2011)CrossRefGoogle Scholar
  6. 6.
    Cahn, B.R., Polich, J.: Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychol. Bull. 132(2), 180 (2006)CrossRefGoogle Scholar
  7. 7.
    Ahani, A., et al.: Quantitative change of EEG and respiration signals during mindfulness meditation. J. Neuroeng. Rehabil. 11(1), 87 (2014)CrossRefGoogle Scholar
  8. 8.
    Xue, S.-W., et al.: Short-term meditation induces changes in brain resting EEG theta networks. Brain Cogn. 87, 1–6 (2014)CrossRefGoogle Scholar
  9. 9.
    Travis, F., Shear, J.: Focused attention, open monitoring and automatic self-transcending: categories to organize meditations from Vedic, Buddhist and Chinese traditions. Conscious. Cogn. 19(4), 1110–1118 (2010)CrossRefGoogle Scholar
  10. 10.
    Ahani, A., et al.: Change in physiological signals during mindfulness meditation. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE (2013)Google Scholar
  11. 11.
    Surangsrirat, D., Intarapanich, A.: Analysis of the meditation brainwave from consumer EEG device. In: SoutheastCon 2015. IEEE (2015)Google Scholar
  12. 12.
    Lutz, A., et al.: Attention regulation and monitoring in meditation. Trends Cogn. Sci. 12(4), 163–169 (2008)CrossRefGoogle Scholar
  13. 13.
    Kaur, C., Singh, P.: EEG derived neuronal dynamics during meditation: progress and challenges. Adv. Prev. Med. 2015 (2015)Google Scholar
  14. 14.
    Dissanayaka, C., et al.: Comparison between human awake, meditation and drowsiness EEG activities based on directed transfer function and MVDR coherence methods. Med. Biol. Eng. Comput. 53(7), 599–607 (2015)CrossRefGoogle Scholar
  15. 15.
    Jadhav, N., Manthalkar, R., Joshi, Y.: Effect of meditation on emotional response: an EEG-based study. Biomed. Signal Process. Control 34, 101–113 (2017)CrossRefGoogle Scholar
  16. 16.
    Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from EEG using higher order crossings. IEEE Trans. Inf. Technol. Biomed. 14(2), 186–197 (2010)CrossRefGoogle Scholar
  17. 17.
    Kedem, B., Yakowitz, S.: Time Series Analysis by Higher Order Crossings. IEEE press, New York (1994)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shruti Phutke
    • 1
  • Narendra Jadhav
    • 1
  • Ramchandra Manthalkar
    • 1
  • Yashwant Joshi
    • 1
  1. 1.Centre of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and TechnologyNandedIndia

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