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An Online Monitoring System for Measuring Human Attention Level Based on Brain Activities

  • Haitham Mohammed Al Balushi
  • Satish Masthenahally Nachappa
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 958)

Abstract

One of the major organ and resource in human is brain which is interconnected with millions of neurons, these neurons can be used to know the attention of human at different time intervals. The human brain waves plays a vital role in controlling and monitoring the devices and environment. The EEG sensor is used to transfer brain waves using Bluetooth and automated through a microcontroller for knowing the attention of the Human brain. The major system in the designed work is with Arduino Mega Microcontroller which is programmed to monitor the attention of the brain activities. All the readings helps in predicting the status of the human brain Activity. The quality of the work is defined by recording the mental states of human brain by different frequencies of brain waves. The different frequencies are detected are alpha, beta, gamma and delta patterns. In the carried work real time simulation and testing is done on breadboard. The implementation results are clearly shown by signifying each step importance. The outcomes of the carried work are monitored in real time and checked through Internet of things (IOT). The carried work is tested on different participants and they were advised to analyze and think on various tasks such as left, right, forward and back. The data is although collected from various participants for correlation and deviation values. In general the analysis is performed with the thinking data and computed in terms of attention levels. Further to this it can be used for Alzheimer’s disease and cognitive impaired people to know the exact thinking of the people and their needs or requirements.

Keywords

EEG Arduino IOT Brain Attention Alpha 

Notes

Acknowledgment

I am very much thankful to Middle East College, Muscat for providing me lab facilities and also creating an environment of renowned professors who helped me at every step during contribution to my work and defining me new research findings related to the work proposed.

References

  1. 1.
    Chen, C.M., Wang, J.Y., Yu, C.M.: Assessing the attention levels of students by using a novel attention aware system based on brainwave signals. Br. J. Educ. Technol. 48(2), 348–369 (2017).  https://doi.org/10.1111/bjet.12359CrossRefGoogle Scholar
  2. 2.
    Frey, J., et al.: Framework for Electroencephalography-Based Evaluation of User Experience (2016).  https://doi.org/10.1145/2858036.2858525
  3. 3.
    Mathewson, K.J., et al.: Regional electroencephalogram (EEG) alpha power and asymmetry in older adults: a study of short-term test-retest reliability. Front. Aging Neurosci. 7(9), 1–10 (2015).  https://doi.org/10.3389/fnagi.2015.00177CrossRefGoogle Scholar
  4. 4.
    Mokhtar, R., et al.: Assessing Attention and Meditation Levels in Learning Process Using Brain Computer Interface, pp. 3–7 (n.d.)Google Scholar
  5. 5.
    Rebolledo-Mendez, G., et al.: Assessing NeuroSky’s usability to detect attention levels in an assessment exercise. In: Jacko, J.A. (ed.) HCI 2009. LNCS, vol. 5610, pp. 149–158. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02574-7_17CrossRefGoogle Scholar
  6. 6.
    Yang, S.-M., Chen, C.-M., Yu, C.-M.: Assessing the attention levels of students by using a novel attention aware system based on brainwave signals. In: 2015 IIAI 4th International Congress on Advanced Applied Informatics, pp. 379–384 (2015).  https://doi.org/10.1109/iiai-aai.2015.224
  7. 7.
    Li, K.: P300 Based Single Trial Independent Component Analysis on EEG Signal (2009). http://dl.acm.org/citation.cfm?id=1611130. Accessed 19 July 2009
  8. 8.
    Campisi, P.: Brain waves for automatic biometric-based user recognition (2014). http://dl.acm.org/citation.cfm?id=2714006. Accessed May 2014
  9. 9.
    Clodoaldo A.M.: Kernel Machines for Epilepsy Diagnosis via EEG Signal Classification (2011). http://dl.acm.org/citation.cfm?id=2031305. Accessed 2 Oct 2011
  10. 10.
    Robbins, R.: Investigating the Neurosky Mindwave EEG Headset. http://dl.acm.org/citation.cfm?id=2031305. Accessed 2 Oct 2014
  11. 11.
    Asif Hussain, S., Giri Prasad, M.N., Ramaiah, C.: An intelligent frame work system for finger touch association on planar surfaces. In: Attele, K.R., Kumar, A., Sankar, V., Rao, N.V., Sarma, T.H. (eds.) Emerging Trends in Electrical, Communications and Information Technologies. LNEE, vol. 394, pp. 185–191. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-1540-3_19CrossRefGoogle Scholar
  12. 12.
    Campisi, P., La Rocca, D.: Brain waves for automatic biometric-based user recognition. IEEE Trans. Inf. Forens. Secur. 9(5), 782–800 (2014)CrossRefGoogle Scholar
  13. 13.
    Abbas, A., Lee, C.J., Kim, K.-I.: Delay bounded Spray and wait in delay tolerant networks. In: Proceedings of the 9th International Conference on Ubiquitos Information Management and communication, Bali, Indonesia, 08–10 January 2015. ACM, New York (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Haitham Mohammed Al Balushi
    • 1
  • Satish Masthenahally Nachappa
    • 1
  1. 1.Middle East CollegeMuscatSultanate of Oman

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