Effect of eye massaging device towards brain rhythms

  • Authors

    • F. A. Mansor
    • R. Sudirman
    https://doi.org/10.14419/ijet.v7i4.44.26869

    Received date: January 31, 2019

    Accepted date: January 31, 2019

    Published date: December 1, 2018

  • electroencephalography, eye massaging device, mind relaxation state, relative alpha power, t-test analysis
  • Abstract

    This study aimed to examine the effectiveness of the eye massaging device in promoting mind relaxation state based on alpha rhythm in
    brain. This study has been conducted involving 35 participants whose their brain signals were captured by an electroencephalography
    (EEG) machine whilst they undergoing resting state, eye massaging session and visual tasks designed to induced stress. The signal is
    analyzed using Discrete Wavelet Transform (DWT) and the major concern of the study, relative power of alpha was extracted from
    channel point O1 and O2. T-test analysis used to validate the differences of relative alpha power produced before and after using the
    device. Based on the results, the average values of relative alpha power collected for EEG signals before using the eye massaging device
    has been increased for both channel (O1: before = 17.601, after = 19.765, O2: before = 12.577, after = 14.783) with an increment of
    12.29% and 17.54% respectively. However, there is no enough evidence to prove that using the device give positive effect on improving
    participant’s relaxation state as there is only have 51.5% and 58.4% chances that the device might worked on mind relaxation state.

  • References

    1. B. R. Frueh, A. Hayes, G. S. Lynch and D. A. Williams, "Con-tractile Properties and Temperature Sensitivity of the Extraocu-lar Muscles, the Levator and Superior Rectus of the Rabbit", Journal of Physiology, (1994), pp.327-336.
    2. A. Bulling, J. A. Ward, H. Gellersen and G. Troster, "Eye Movement Analysis for Activity Recognition using Electroocu-lography", IEEE Transaction on Pattern Analysis and Machine Intelligence, 33(4), (2011), pp.741-753.
    3. J. G. Betts, P. Desaix, E. Johnson, J. E. Johnson, O. Korol, D. Kruse, B. Poe, J. A. Wise, M. Womble and K. A. Young, "Anatomy and Physiology", OpenStax College, Rice University, Texas, (2017), pp.464-466.
    4. "Hand Phone Users Survey 2017", Malaysian Communications and Multimedia Commission, (2017), pp.9 & 54-55.
    5. M. Prensky, "Adopt and Adapt School Technology in the 21st Century", (2005), Edutopia.
    6. "Internet Users Survey 2017", Malaysian Communications and Multimedia Commission, (2017), pp.9 & 12-13.
    7. N. M. Rosley, I. Ismail and H. L. Visvernardan, "Students' Ac-ceptance on Mobile Phone Usage and SMS Learning", Malay-sian Journal of Distance Education, 13(2), (2011), pp.49-59.
    8. D. C. Rizzo, “Introduction to Anatomy and Physiology”, Cen-gage Learning, (2011).
    9. M. S. Sweeney, “Brain: The Complete Mind: How it Develops, How it Works, and How to Keep it Sharp”, (2009), National Geo-graphic.
    10. "CleveLabs Laboratory Course System Version 6.0", Cleveland Medical Devices Inc., Cleveland, OH, (2006), pp.1-11.
    11. E. Niedermeyer and F. L. Silva, “Electroencephalography: Basic Principles, Clinical Applications, and Related Fields”, 5th Ed, Phil-adelphia: Lippincott Williams & Wilkins (2005).
    12. S. Sanei and J. A. Chambers, “EEG Signal Processing”, John Wiley & Sons, Ltd, (2007), pp.11, 15-16.
    13. "Human Electro-oculography (EOG)", Teaching Instrument, ADInstruments, (2004), pp.1-8.
    14. A. Subasi and E. Ercelebi, "Classification of EEG Signals using Neural Network and Logistic Regression", Computer Methods and Programs in Biomedicine, 78, (2005), pp.87-99.
    15. Z. M. Khalid, N. M. Ismail, A. Bahar, I. Mohamad, M. H. Lee, N. Ismail and N. Ahmad, “Introductory Statistics for Engineering Stu-dents”, Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, (2012), pp.93-94.
    16. A. R. Clarke, R. J. Barry, R. McCarthy and M. Selikowitz, "Excess Beta Activity in Children with Attention-deficit/Hyperactivity Dis-order an Atypical Electrophysiological Group", Psychiatry Re-search, 103(2-3), (2001), pp.527-538.
    17. H-L. Fu and T-M. Kuan, “Under Different Conditions of Learning Memory in the Electroencephalograph (EEG) Analysis and Discus-sion”, 2nd International Conference on Power Electronics and Intel-ligent Transportation System, (2009), pp.352-355.
  • Downloads

  • How to Cite

    A. Mansor, F., & Sudirman, R. (2018). Effect of eye massaging device towards brain rhythms. International Journal of Engineering and Technology, 7(4.44), 88-93. https://doi.org/10.14419/ijet.v7i4.44.26869