A study on wavelet analysis of SSVEP Signals

  • Authors

    • Bincy Babu
    • R Chandrasekaran
    • Josline Elsa Joseph
    • Thella Shalem Rahul
    • T R Thamizhvani
    • A Josephin Arockia Dhivya
    2018-05-03
    https://doi.org/10.14419/ijet.v7i2.25.12354
  • BCI, EEG, Power spectral density, SSVEP, Wavelet

  • Abstract

    Almost every Brain Control Interfcae (BCI) system is framed based on Steady State Visual Evoked Potential (SSVEP) which is predicted through distinguishing overriding frequency components in Electroencephalography (EEG) signals. The proposed system aims in accurate feature extraction of SSVEP signals. Power spectral analysis and wavelet analysis are done for feature analysis. The feature set variation for male and female subjects are obtained. Compared power spectral estimation and wavelet analysis, merits and demerits of each approach can be identified from the outcomes. It offers a theoretical reference of practical choice for BCI application.

     

     

  • References

    1. [1] Regan D, “Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine,†J. Clin. Neurophysiol., 7: 450-451, 1990.

      [2] Pasqualotto E, Federici S and Belardinelli M O, “Toward functioning and usable brain-computer interfaces (BCIs): a literature review,â€. Rehabil. Assist. Technol., 7: 89-103, 2012.

      [3] Bin G Y, Gao X R, Yan Z, Hong B and Gao S K, “An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method,†J. Neural. Eng., 6: 046002, 2009.

      [4] Diez P F, Mut V A, Avila Perona E M and Laciar Leber E, “Asynchronous BCI control using high-frequency SSVEP,†J. Neuroeng. Rehabil., 8: 39, 2011.

      [5] Wong C M, Wang B Y, Wan F, Mak P U, Mak P I and Vai M I, “An improved phase-tagged stimuli generation method in steady-state visual evoked potential based brain-computer interface,†Proc. 3rd Int. Conf. on Biomedical Engineering and Informatics, 2: 745-749, 2010.

      [6] Muller-Putz G R and Pfurtscheller, “Control of an electrical prosthesis with an SSVEP-based BCI,†IEEE Trans. Biomed. Eng., 55: 361-364, 2008.

      [7] Li Zhao, Yong Sun, Research on Mobile Phone Dialing System Based on Steady-State Visual Evoked Potential, Chinese Journal of Biomedical Engineering,Vol.32,No.2,244-246,2013.

      [8] Diez P F, Torres Müller S M, Mut V A, Laciar E, Avila E, Bastos-Filho T F and Sarcinelli-Filho M, “Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface,†Med. Eng. Phys., 35: 1155-1164, 2013.

      [9] Yan B, Li Z, Li H, Yang G and Shen H, “Research on brain- computer interface technology based on steady state visual evoked potentials,†Proc. 4th Int. Conf. on Bioinformatics and Biomedical Engineering, 1-4, 2010.

      [10] Bian Y, Li H W, Zhao L, Yang G H and Geng L Q, “Research on steady state visual evoked potentials based on wavelet packet technology for brain-computer interface,†Proc. Eng., 15: 2629-2633, 2011.

      [11] Zhang Z, Li X and Deng Z, “A CWT-based SSVEP classification method for brain-computer interface system,†Proc. Int. Conf. on Intelligent Control and Information Processing, 43-48, 2010.

      [12] P L Lee, Chang H C, Hsieh T Y, Deng H T and Sun C W, “A brain-wave-actuated small robot car using ensemble empirical mode decomposition-based approach,†IEEE Trans. Syst. Man Cybern. Syst. Hum., 42: 1053-1064, 2012.

      [13] Garcia-Molina G, Zhu D H and Abtahi S, “Phase detection in a visual-evoked-potential based brain computer interface,†Proc. 18th European Signal Processing Conf., 949-953, 2010.

      [14] Zhu D H, Garcia-Molina G, Mihajlović V and Aartsl R M, “Phase synchrony analysis for SSVEP-based BCIs,†Proc. 2nd Int. Conf. on Computer Engineering and Technology, 2: V2329- V2333, 2010.

      [15] Zhao L, Yuan P X, Xiao L T, Meng Q G, Hu D F and Shen H, “Research on SSVEP feature extraction based on HHT,†Proc. 7th Int. Conf. on Fuzzy Systems and Knowledge Discovery, 5: 2220-2223, 2010

      [16] Zhu D, Garcia-Molina G, Mihajlović V and Aarts R M, “Online BCI implementation of high-frequency phase modulated visual stimuli,†Universal Access in Human- Computer Interaction. Users Diversity, 6766: 645-654, 2011.

      [17] Friman O, Luth T, Volosyak I and Graser A, “Spelling with steady-state visual evoked potentials,†Proc. 3rd Int. IEEE/EMBS Conf. on Neural Eng., 354-357, 2007.

      [18] Müller S M T, A. M. F. L. M. De Sá, Bastos-Filho T F and Sarcinelli-Filho M, “Spectral techniques for incremental SSVEP analysis applied to a BCI implementation,†Proc. 5th Latin American Congress on Biomedical Engineering, 33: 1090-1093, 2013.

      [19] Muller S M T, Bastos T F and Sarcinelli M, “Incremental SSVEP analysis for BCI implementation,†Proc. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 3333-3336, 2010.

      [20] Chang H C, Deng H T, Lee P L, Wu C H and Shyu K K, “Real-time control of an SSVEP-actuated remote-controlled car,†Proc. SICE Annual Conf., 1884-1887, 2010.

      [21] https://physionet.org/physiobank/database/mssvepdb

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  • How to Cite

    Babu, B., Chandrasekaran, R., Elsa Joseph, J., Shalem Rahul, T., R Thamizhvani, T., & Josephin Arockia Dhivya, A. (2018). A study on wavelet analysis of SSVEP Signals. International Journal of Engineering & Technology, 7(2.25), 10-13. https://doi.org/10.14419/ijet.v7i2.25.12354

    Received date: 2018-05-03

    Accepted date: 2018-05-03

    Published date: 2018-05-03