EEG signal denoising based on wavelet transform
Harender, R. K. Sharma · 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) · 2017
Low amplitude EEG signal are easily affected by various noise sources. This work presents de-noising methods based on the combination of stationary wavelet transform (SWT), universal threshold, statistical threshold and Discrete Wavelet Transform (DWT) with symlet, haar, coif, and bior4.4 wavelets. The results show significant improvement in performance parameter such as Signal to Artifacts ratio (SAR), Correlation Coefficient (CC) and Normalized Mean Squared error (NMSE). Simulink has been used to model DWT based de noising of EEG signal implementable on FPGA with Xilinx System Generator.