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Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface

Author

Listed:
  • Omar Trigui

    (Advanced Technologies for Medicine and Signals ‘ATMS', ENIS, Sfax University, Sfax, Tunisia)

  • Wassim Zouch

    (King Abdulaziz University (KAU) Jeddah, Saudi Arabia & Advanced Technologies for Medicine and Signals ‘ATMS', ENIS, Sfax University, Sfax, Tunisia)

  • Mohamed Ben Messaoud

    (Advanced Technologies for Medicine and Signals ‘ATMS', ENIS, Sfax University, Sfax, Tunisia)

Abstract

The features extraction is the main step in a Brain-Computer Interface (BCI) design. Its goal is to create features easy to be interpreted in order to produce the most accurate control commands. For this end, these features must include all the original signal characteristics. The generated brain's signals' non-stationary and nonlinearity constitute a limitation to the improvement of the performances of systems based on traditional signal processing such as Fourier Transform. This work deals with the comparison of features extraction between Hilbert-Huang Transform (HHT) and Welch's method for Power Spectral Density estimation (PSD) then on the creation of an adaptive method combining the two. The parameters optimization of each method is firstly performed to reach the best classification accuracy rate. The study shows that the PSD estimation is sensitive to the parametric variation whereas the HHT method is mainly robust. The classification results show that an adaptive joint method can reach 90% of accuracy rate for a mental activity period of 1s.

Suggested Citation

  • Omar Trigui & Wassim Zouch & Mohamed Ben Messaoud, 2017. "Hilbert-Huang Transform and Welch's Method for Motor imagery based Brain Computer Interface," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 11(3), pages 47-68, July.
    • Handle: RePEc:igg:jcini0:v:11:y:2017:i:3:p:47-68
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