A survey on different discrete wavelet transforms and thresholding techniques for EEG denoising

Abstract

EEG is widely used to record the electrical activity of the brain for detecting various kinds of diseases and disorders, related to the malfunctioning of the human brain. EEG signals are contaminated with several unwanted artifacts during EEG recording and these artifacts make the analysis of EEG signal difficult by hiding some valuable information. EMG noise or noise due to muscle activities is most common artifact. In this paper, EMG noised corrupt EEG signal is denoised using five different WT techniques like DWT, DT-DWT, DD-DWT & DDDT-DWT with five different thresholding techniques like Hard Thresholding, Soft Thresholding, Semi-Soft Thresholding & Neighboring Coefficients Thresholding. The performance of these methods is compared by computing Root Mean Square Error and Signal to Noise Ratio. Results show that DDDT-DWT, with Soft Thresholding, provides the least RMSE and highest SNR for the removal of EMG noise.