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JACIII Vol.22 No.3 pp. 306-315
doi: 10.20965/jaciii.2018.p0306
(2018)

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Wavelet Transform Analysis the Recognizing Brain Activities for Development the Palm-Size and Simplification Near-Infrared Spectroscopy Prototype System by Using Arduino

Yukinobu Hoshino*, Masayuki Kubo*, and Thang Cao**

*Kochi University of Technology,
185 Tosayamada Miyanokuchi, Kami, Kochi 782-0003, Japan

**The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Received:
January 20, 2017
Accepted:
February 9, 2018
Published:
May 20, 2018
Creative Commons License
Keywords:
wavelet transform analysis, functional near-infrared spectroscopy (fNIRS), brain-computer interface (BCI).
Abstract

Functional near-infrared spectroscopy (fNIRS) and brain computer interface (BCI) have become indispensable tools for recording and monitoring brain activity, comprising a non-invasive and safe technique that allows researchers to monitor blood flow in the front part of the brain. Although some medical device manufacturers developed complex fNIRS systems, downsized fNIRS systems are important for other uses, such as in portable (palm-sized) and wearable healthcare devices. This paper proposes a downsized compact fNIRS prototype that detects hemodynamics in the frontal lobe. The aim is to develop a compact fNIRS system, which is reliable and easy to integrate into portable (palm-sized) BCI devices. Through practical experiments with human subjects, our proposed system showed an ability to detect and monitor the start and end time of human brain activities when participants were solving a calculation table.

Cite this article as:
Y. Hoshino, M. Kubo, and T. Cao, “Wavelet Transform Analysis the Recognizing Brain Activities for Development the Palm-Size and Simplification Near-Infrared Spectroscopy Prototype System by Using Arduino,” J. Adv. Comput. Intell. Intell. Inform., Vol.22 No.3, pp. 306-315, 2018.
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References
  1. [1] J. J. Vidal, “Real-Time Detection of Brain Events in EEG,” Proc. of the IEEE, Vol.65, No.5, pp. 633-641, 1977.
  2. [2] J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-Computer Interfaces for Communication and Control,” Clinical Neurophysiology, Vol.113, No.6, pp. 767-791, 2002.
  3. [3] S. Okasaka and Y. Hoshino, “Development of Estimation Method about Activity States for NIRS-Based BCI System,” 2012 Joint 6th Int. Conf. on Soft Computing and Intelligent Systems (SCIS) and 13th Int. Symposium on Advanced Intelligent Systems (ISIS), pp. 1144-1149, 2012.
  4. [4] B. Koo, H. Vu, H.-G. Lee, H.-C. Shin, and S. Choi, “Motor Imagery Detection with Wavelet Analysis for NIRS-Based BCI,” 2016 4th Int. Winter Conf. on Brain-Computer Interface (BCI), pp. 1-4, 2016.
  5. [5] M. Sawan, M. T. Salam, J. Le Lan, A. Kassab, S. Gélinas, P. Vannasing, F. Lesage, M. Lassonde, and D. K. Nguyen, “Wireless Recording Systems: from Noninvasive EEG-NIRS to Invasive EEG Devices,” IEEE Trans. on Biomedical Circuits and Systems, Vol.7, No.2, pp. 186-195, 2013.
  6. [6] R. Zimmermann, L. Marchal-Crespo, J. Edelmann, O. Lambercy, M.-C. Fluet, R. Riener, M. Wolf, and R. Gassert, “Detection of Motor Execution Using a Hybrid fNIRS-Biosignal BCI: a Feasibility Study,” J. of Neuroengineering and Rehabilitation, Vol.10, No.1, p. 4, 2013.
  7. [7] N. Naseer and K.-S. Hong, “Classification of Functional Near-Infrared Spectroscopy Signals Corresponding to the Right- and Left-Wrist Motor Imagery for Development of a Brain-Computer Interface,” Neuroscience Letters, Vol.553, pp. 84-89, 2013.
  8. [8] N. Naseer and K.-S. Hong, “Corrigendum fNIRS-Based Brain-Computer Interfaces: a Review,” Frontiers in Human Neuroscience, Vol.9, pp. 172-172, 2015.
  9. [9] K.-S. Hong, N. Naseer, and Y.-H. Kim, “Classification of Prefrontal and Motor Cortex Signals for Three-Class fNIRS–BCI,” Neuroscience Letters, Vol.587, pp. 87-92, 2015.
  10. [10] Internatinal Commission on Illumination, “International Lightning Vocabulary,” 1987.
  11. [11] E. Okada and D. T. Delpy, “Near-Infrared Light Propagation in an Adult Head Model, II. Effect of Superficial Tissue Thickness on the Sensitivity of the Near-Infrared Spectroscopy Signal,” Applied Optics, Vol.42, No.16, pp. 2915-2921, 2003.
  12. [12] International Commission on Non-Ionizing Radiation Protection, “ICNIRP Guidelines on Limits of Exposure to Incoherent Visible and Infrared Radiation,” Health Physics, Vol.105, No.1, pp. 74-96, 2013.
  13. [13] I. E. Commission, “Photobiological Safety of Lamps and Lamp Systems,” 2006
  14. [14] I. E. Commission, “Safety of Laser Products - Part 1: Equipment Classification and Requirements,” 2014.
  15. [15] N. Kourkoumelis and M. Tzaphlidou, “Eye Safety Related to Near Infrared Radiation Exposure to Biometric Devices,” The Scientific World J., Vol.11, pp. 520-528, 2011.
  16. [16] C. J. Soraghan, T. E. Ward, F. Matthews, and C. Markham, “Optical Safety Assessment of a Near-Infrared Brain-Computer Interface,” IET Irish Signals and Systems Conf. (ISSC 2008), 2008.
  17. [17] R. Beisteiner, P. Höllinger, G. Lindinger, W. Lang, and A. Berthoz, “Mental Representations of Movements. Brain Potentials Associated with Imagination of Hand Movements,” Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section, Vol.96, No.2, pp. 183-193, 1995.
  18. [18] F. F. Jobsis, “Noninvasive, Infrared Monitoring of Cerebral and Myocardial Oxygen Sufficiency and Circulatory Parameters,” Science, Vo.198, No.4323, pp. 1264-1267, 1977.
  19. [19] Y. Ito, R. P. Kennan, E. Watanabe, and H. Koizumi, “Assessment of Heating Effects in Skin During Continuous Wave Near Infrared Spectroscopy,” J. of Biomedical Optics, Vol.5, No.4, pp. 383-390, 2000.
  20. [20] F. Normandin, M. Sawan, and J. Faubert, “A New Integrated Front-End for a Noninvasive Brain Imaging System Based on Near-Infrared Spectroreflectometry,” IEEE Trans. on Circuits and Systems I: Regular Papers, Vol.52, No.12, pp. 2663-2671, 2005.
  21. [21] F. Chenier and M. Sawan, “A New Brain Imaging Device Based on fNIRS,” IEEE Biomedical Circuits and Systems Conf. BIOCAS 2007, pp. 1-4, 2007.
  22. [22] B. C. Michael, S. Patterson, and B. C. Wilson, “Time Resolved Reflectance and Transmittance for the Noninvasive Measurement of Tissue Optical Properties,” Vol.28, pp. 2331-2336, 1989.
  23. [23] P. V. d. Zee, M. Cope, S. Arridge, M. Essenpreis, L. Potter, A. Edwards, J. Wyatt, D. McCormick, S. Roth, E. Reynolds, and D. Delpy, “Experimentally Measured Optical Pathlengths for the Adult Head, Calf and Forearm and the Head of the Newborn Infant as a Function of Inter Optode Spacing,” Oxygen Transport to Tissue XIII, pp. 143-153, Springer, 1992.
  24. [24] C. Hock, F. Müller-Spahn, S. Schuh-Hofer, M. Hofmann, U. Dirnagl, and A. Villringer, “Age Dependency of Changes in Cerebral Hemoglobin Oxygenation during Brain Activation: A Near-Infrared Spectroscopy Study,” J. of Cerebral Blood Flow & Metabolism, Vol.15, No.6, pp. 1103-1108, 1995.
  25. [25] K. Tai and T. Chau, “Single-Trial Classification of NIRS Signals During Emotional Induction Tasks: Towards a Corporeal Machine Interface,” J. of Neuroengineering and Rehabilitation, Vol.6, No.1, p. 39, 2009.
  26. [26] M. Verner, M. J. Herrmann, S. J. Troche, C. M. Roebers, and T. H. Rammsayer, “Cortical Oxygen Consumption in Mental Arithmetic as a Function of Task Difficulty: a Near-Infrared Spectroscopy Approach,” Frontiers in Human Neuroscience, Vol.7, 2013.
  27. [27] H. Tsunashima and K. Yanagisawa, “Measurement of Brain Function of Car Driver Using Functional Near-Infrared Spectroscopy (fNIRS),” Computational Intelligence and Neuroscience, Vol.2009, 2009.
  28. [28] L. Xu, B. Wang, G. Xu, W. Wang, Z. Liu, and Z. Li, “Functional Connectivity Analysis Using fNIRS in Healthy Subjects During Prolonged Simulated Driving,” Neuroscience Letters, Vol.640, pp. 21-28, 2017.
  29. [29] H. Sato, N. Tanaka, M. Uchida, Y. Hirabayashi, M. Kanai, T. Ashida, I. Konishi, and A. Maki, “Wavelet Analysis for Detecting Body-Movement Artifacts in Optical Topography Signals,” Neuroimage, Vol.33, No.2, pp. 580-587, 2006.
  30. [30] S. J. Matcher, P. J. Kirkpatrick, K. Nahid, M. Cope, and D. T. Delpy, “Absolute Quantification Methods in Tissue Near-Infrared Spectroscopy,” Int. Society for Optics and Photonics, West’95, pp. 486-495, 1995.
  31. [31] Y. Hoshi, “Functional Near-Infrared Spectroscopy: Current Status and Future Prospects,” J. of Biomedical Optics, Vol.12, No.6, p. 062106, 2007.

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