An adaptive noise removal method for EEG signals

Abstract

EEG signals are the response to the electrophysiological activity of brain nerve cells in the cerebral cortex, but the collected EEG signal generally contains noise. In order to effectively remove the noise and retain useful information, after research and analysis, this paper proposes an improved EEG noise-removing algorithm based on the threshold method where wavelet contraction is used. The improved adaptive threshold selection algorithm makes it possible that the threshold changes with the number of layers of decomposed signal, so it can be applied flexibly in practice. The wavelet transform is used to decompose the EEG signal into multiple layers of high- and low-frequency coefficients. Adaptive threshold processing is then applied to the wavelet coefficients in accordance with the various decomposition levels, and the scaled wavelet coefficients are then reconstituted to produce the denoised EEG signal. Using the Root Mean Square Error (RMSE) and Signal Noise Ratio (SNR) as quantitative measures of the denoising effect Through trials, the improved threshold method, hard and soft threshold method, and adaptive threshold method were contrasted. The experimental findings demonstrate that the wavelet shrinkage-based improved threshold approach has a better denoising effect than the other three threshold methods.