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A Hybrid Optimization Method OWGWA for EEG/ERP Adaptive Noise Canceller With Controlled Search Space

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  • Rachana Nagal

    (Amity University, Noida, India)

  • Pradeep Kumar

    (Amity University, Noida, India)

  • Poonam Bansal

    (Maharaja Surajmal Institute of Technology, Delhi, India)

Abstract

In this paper, a system for filtering event-related potentials/electroencephalograph is exhibited by adaptive noise canceller through an optimization algorithm, oppositional hybrid whale-grey wolf optimization algorithm (OWGWA). The OWGWA can choose the control parameters of the grey wolf algorithm utilizing whale parameters. To balance out the randomness of optimization strategies another methodology is implemented called controlled search space. Adaptive filter's noise reduction capability has been tested through adding adaptive white Gaussian noise over contaminated EEG signals at different noise levels. The performance of the proposed OWGWA-CSS algorithm is evaluated by signal to noise ratio in dB, mean value, and the relationship between resultant and input ERP. In this work, ANCs are also implemented by utilizing other optimization techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWGWA-CSS technique provides higher SNR value, significantly lower mean and higher correlation as compared to other techniques.

Suggested Citation

  • Rachana Nagal & Pradeep Kumar & Poonam Bansal, 2020. "A Hybrid Optimization Method OWGWA for EEG/ERP Adaptive Noise Canceller With Controlled Search Space," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 11(3), pages 30-48, July.
    • Handle: RePEc:igg:jsir00:v:11:y:2020:i:3:p:30-48
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