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Steady-State Visual Evoked Potential-Based Real-Time BCI for Smart Appliance Control

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Book cover Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 768))

Abstract

Brain–Computer Interface (BCI) provides an alternative way for humans to communicate with the external environment. BCI systems can be of great help to people with severe motor disabilities who cannot perform normal daily activities. In this paper, we introduce a novel steady-state visual evoked potential (SSVEP)-based brain–computer interface system that control home appliances like electric fan, tube light, etc. Designed system aim is to extract the SSVEP signal and then classify them using PCA. We confirmed the generation of SSVEP frequencies in the online analysis using Fast Fourier Transform. The classification of SSVEP signals is done using Principal Component Analysis.

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References

  1. McFarland, D.J., Wolpaw, J.R.: Brain-computer interfaces for communication and control. Commun. ACM 54(5) (2011)

    Google Scholar 

  2. Fazel-Rezai, R., et al.: P300 brain computer interface: current challenges and emerging trends. Frontiers Neuroeng. 5, 14 (2012)

    Google Scholar 

  3. Joshi, R., Saraswat, P., Gajendran, R.: A novel mu rhythm-based brain computer interface design that uses a programmable system on chip. J. Med. Signals Sens. 2(1), 11 (2012)

    Google Scholar 

  4. Neuper, C., Muller-Putz, G.R., Scherer, R.: Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog. Brain Res. 159, 393–409 (2006)

    Google Scholar 

  5. Muller-Putz, G.R., Scherer, R., Neuper, C.: Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? IEEE Trans. Neural Syst. Rehabil. Eng. 14(1) (2006)

    Google Scholar 

  6. Kaufmann, T., Herweg, A., Kübler, A: Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials. J. NeuroEng. Rehabil. 11(1), 7 (2014)

    Google Scholar 

  7. Kaongoen, N., Jo, S.: A novel hybrid auditory BCI paradigm combining ASSR and P300. J. Neurosci. Methods 279, 44–51 (2017)

    Google Scholar 

  8. Norcia, A.M., et al.: The steady-state visual evoked potential in vision research: a review. J. Vision 15(6), 4 (2015)

    Google Scholar 

  9. Morris, J., Holcomb, P.J.: Effects of spatial selective attention on the steady-state visual evoked potential in the 20–28 Hz range. Brain Res. Cogn. Brain Res. 25(3) (1998)

    Google Scholar 

  10. Movahedi, M.M., Mehdizadeh, A., Alipour, A.: Development of a Brain Computer Interface (BCI) speller system based on SSVEP signals. J. Biomed. Phys. Eng. 3(3) (2013)

    Google Scholar 

  11. Chen, X., Chen, Z.: A high-ITR SSVEP-based BCI speller. Brain-Comput. Interfaces (Taylor & Franics Online) 1 (2014)

    Google Scholar 

  12. Zhu, D., Bieger, J.: A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. 2010, Article ID 702357, 12 (2010)

    Google Scholar 

  13. Myslobodsky, M.S.; Coppola, R., Bar-Ziv, J., Weinberger, D.R.: Adequacy of the international 10–20 electrode system for computed neurophysiologic topography. J. Clin. Neurophysiol. 7, 507–518 (1990)

    Google Scholar 

  14. Zifkin, B.G., Inoue, Y.: Visual reflex seizures induced by complex stimuli. Epilepsia 45, 27–29 (2004)

    Google Scholar 

  15. Lin, Z., Zhang, C., Wu, W.: Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans. Biomed. Eng. 54(6), 1172–1176 (2007)

    Google Scholar 

  16. Nakanishi, M., et al.: A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials. In: Yao, D. (ed.) PLoSONE 10(10) (2015)

    Google Scholar 

  17. Wu, Z.: SSVEP extraction based on the similarity of background EEG. In: Di Russo, F. (ed.) PLoS ONE 9(4) (2014)

    Google Scholar 

  18. Jolliffe, I.: Principal Component Analysis. Springer, New York (1986)

    Book  Google Scholar 

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Acknowledgements

Authors would like to acknowledge financial assistance from Department of Science and Technology (New Delhi) under Instrument Development Scheme. Author Mr. Noel Tavares would like to thank DST, New Delhi for granting INSPIRE Fellowship to do full time research at Goa University.

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Authors and Affiliations

  1. Department of Electronics, Goa University, Taleigao Plateau, Goa, India

    Noel G. Tavares & R. S. Gad

Authors
  1. Noel G. Tavares
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  2. R. S. Gad
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Corresponding author

Correspondence to R. S. Gad .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India

    Pradeep Kumar Mallick

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, India

    Akash Kumar Bhoi

  4. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, United Kingdom

    Ahmed F. Zobaa

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Tavares, N.G., Gad, R.S. (2019). Steady-State Visual Evoked Potential-Based Real-Time BCI for Smart Appliance Control. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_74

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