Performance Analysis of wavelet Thresholding for Denoising EEG Signal - Sinha, Dipali; K, Thangavel (International Journal of Computer Sciences and Engineering, 2025)
Electroencephalogram (EEG) is used for detecting problems in the electrical activity of the brain associated with brain disorders. During acquisition of EEG signals various noises like electrocardiogram (ECG),electromyogram(EMG),electrooculogram(EOG)and power line interference etc. contaminates the signal, which makes the proper analysis of the signal difficult. Therefore, noise removal is an integral part of preprocessing step before signal analysis. In this paper, wavelet transform using different kind of filters like db2, db4, coif2, coif4, sym2 and sym4 is used to decompose the signal into low and high frequency components. Then, high frequency components have been thresholded at each level of decomposition. The denoised signal is reconstructed using the thresholded coefficients and the approximation coefficients. Thresholding methods such as minimaxi, Sure (Heuristic and rigorous) and Square-Root-Log are investigated to compute the threshold value. The coiflet filter at level 4 with minimax thresholding method performed better than other wavelet filters and thresholding methods in terms of Peak Signal-to-Noise Ratio (PSNR) value