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Study and Analysis of a Fast Moving Cursor Control in a Multithreaded Way in Brain Computer Interface

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Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

Abstract

Now a days, ‘Brain Computer Interface’ is one of the fastest growing technologies in which researchers are trying to communicate between human brain and external devices effectively. Generally, brain signal can be captured by EEG technique and the scalp voltage is measured in timely manner. The signal is then transferred to the external devices for modification and finally, the scalp voltage level is transferred into cursor movement (in multithreaded way). Then it will try to reach the desired target inside the computer. In this paper, we propose an efficient technique to reach the cursor to the desired target with single brain signal. This paper also shows that the time and space complexity of this approach is quite less compared to the other existing approaches. If the cursor can reach to the desired target quickly then human can respond very quickly and the application would be more helpful for disabled or paralysed people. For sake of simplicity, the proposed method is also carried out on 5 different amplitude levels of the same signal over different time periods (within 1 ns) in this paper.

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References

  1. Ortiz-Rosario, A., Adeli, H.: Brain-computer interfaces technologies: from signal to action. Rev. Neurosci. 24(3), 537–552 (2013). De Gruyter

    Google Scholar 

  2. Bhattacharyya, S., Khasnobish, A., Chatterjee, S., Konar, A., Tibarewala, D.N.: Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data. In: International Conference on Systems in Medicine and Biology, pp. 126–131. IEEE, IIT Kharagpur, India (2010)

    Google Scholar 

  3. Ramadan, R.A., Refat, S., Elshahed, M.A., Ali, R.A.: Basics of brain computer interface. In: Hassanien, A.E., Azar, A.T. (eds.) Brain-Computer Interfaces. ISRL, vol. 74, pp. 31–50. Springer, Cham (2015). doi:10.1007/978-3-319-10978-7_2

    Google Scholar 

  4. Wolpaw, J.R., McFarland, D.J., Vaughan, T.M.: Brain-computer interface research at the Wadsworth center. IEEE Trans. Rehabil. Eng. 8(2), 222–226 (2012)

    Article  Google Scholar 

  5. Yuan, H., He, B.: Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans. Biomed. Eng. 61(5), 1425–1435 (2014)

    Article  Google Scholar 

  6. Lemm, S., Blankertz, B., Dickhaus, T., Muller, K.-R.: Introduction to machine learning for brain imaging. NeuroImage 56, 387–399 (2011). Elsevier

    Article  Google Scholar 

  7. Alamdari, N., Haider, A., Arefin, R., Verma, A.K., Tavakolian, K., Fazel-Rezai, R.: A review of methods and applications of brain computer interface systems. In: IEEE International Conference on Electro Information Technology (EIT) (2016)

    Google Scholar 

  8. Wolpawa, J.R., Birbaumerc, N., McFarlanda, D.J., Pfurtschellere, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophys. 113, 767–791 (2002). Elsevier

    Article  Google Scholar 

  9. Suleiman, A.B.R., Fatehi, T.A.H.: Features extraction techniques of EEG signal for BCI applications. http://www.limu.edu.ly/pub/papers/f2465.pdf

  10. Birbaumer, N., Cohen, L.G.: Brain computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579(3), 621–636 (2007)

    Article  Google Scholar 

  11. Allison, B.Z., Wolpaw, E.W., Wolpaw, J.R.: Brain-computer interface systems: progress and prospects. Expert Rev. Med. Devices 4(4), 463–474 (2013)

    Article  Google Scholar 

  12. Wolpaw, J.R., McFarland, D.J., Neat, G.W., Forneris, C.A.: An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78, 252–259 (1991). Elsevier

    Article  Google Scholar 

  13. Larsen, E.A.: Classification of EEG signals in a brain-computer interface system, Ph.D. thesis. Norwegian University of Science and Technology, June 2011

    Google Scholar 

  14. SarangShastrakar, M., Bawane, N.G.: BCI based systems to control cursor movement using various techniques: survey report. Int. J. Eng. Sci. 6, 3434 (2016). S. B. Jain Institute of Technology, Management and Research, Nagpur, Maharashtra, India

    Google Scholar 

  15. Classification of 2-Dimensional Cursor Movement Imagery EEG Signals by ÃŰnder Aydemir Department of Electrical and Electronics Engineering, Karadeniz Technical University. IEEE (2016)

    Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering Department, Institute of Engineering and Management, Kolkata, India

    Debashis Das Chakladar & Sanjay Chakraborty

Authors
  1. Debashis Das Chakladar
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  2. Sanjay Chakraborty
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Corresponding author

Correspondence to Debashis Das Chakladar .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Das Chakladar, D., Chakraborty, S. (2017). Study and Analysis of a Fast Moving Cursor Control in a Multithreaded Way in Brain Computer Interface. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_4

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  • DOI: https://doi.org/10.1007/978-981-10-6430-2_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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