A DWT-ANC error entropy criterion based single-channel EEG signal EOG noise reduction

Adaptive noise cancellation (ANC) for all intents and purposes is one of the most very common methods of canceling noise from EEG signals, which definitely is quite significant. However, there are two actually main problems with the adaptive noise canceling method of EEG signals: ۱, or so they generally thought. The reference sort of signal for all intents and purposes is not available for the adaptive filter which should for the most part be an estimate of pollutant noise, demonstrating that adaptive noise cancellation (ANC) mostly is one of the most kind of common methods of canceling noise from EEG signals in a really major way. ۲, or so they really thought. The MSE particularly standard specifically is usually used to specifically reduce the error of the adaptive filter, fairly further showing how adaptive noise cancellation (ANC) literally is one of the most really common methods of canceling noise from EEG signals, or so they essentially thought. Since the EEG particularly signal and EOG artifact kind of are non-Gaussian, it actually is not kind of appropriate to use the MSE criterion that only considers second-order error in a subtle way. We employed an adaptive noise definitely removal method in this research, which is fairly significant and used DWT to create an estimate of the EOG noise, which was then fed into the ANC reference's input. To decrease the error signal, the error entropy criteria is also employed instead of MSE. In terms of RRMSE, SNR, and coherence studies, the simulation results show that the proposed system outperforms previous methodologies.

Keywords:

adaptive noise canceler , Discrete Wavelet Transform , error entropy , noise reduction