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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Khazaei, Ali Akbar
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Computer Science & IT Engineering

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Electroencephalogram denoising using discrete wavelet transform and adaptive noise cancellation based on information theory Abdolahniya, Hashem; Khazaei, Ali Akbar; Azarnoosh, Mahdi; Razavi, Seyed Ehsan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp769-779

Abstract

One of the most frequently used techniques for removing background noise from electroencephalogram (EEG) data is adaptive noise cancellation (ANC). Nonetheless, there exist two primary disadvantages associated with the adaptive noise reduction of EEG signals: the adaptive filter, which is supposed to be an approximation of contaminated noise, lacks the reference signal. The mean squared error (MSE) criterion is frequently employed to achieve this goal in adaptive filters. The MSE criterion, which only considers second-order errors, cannot be used since neither the EEG signal nor the EOG artifact are Gaussian. In this work, we employ an ANC system, deriving an estimate of EOG noise with a discrete wavelet transform (DWT) and input this signal into the reference of the ANC system. The entropy-based error metric is used to reduce the error signal instead of the MSE. Results from computer simulations demonstrate that the suggested system outperforms competing methods with respect to root-mean-square-error, signal-to-noise ratio, and coherence measurements.
Co-Authors Abdolahniya, Hashem Azarnoosh, Mahdi Razavi, Seyed Ehsan