Preprocessing of Electrical Activity in the Brain (Journal of Informatics and Mathematical Sciences, 2022)
Electroencephalography (EEG) is the most effective tool to diagnosis of epileptic diseases. It provides a signal indicated the electrical activity of the brain, which is contaminated by different sources of artefacts and noises. This paper presents a method for removing ocular (EOG) and muscular (EMG) artefacts in the EEG records. The threshold denoising method based on the stationary wavelet transform (SWT) was used to remove these artifacts. The main objective is to improve the quality of the signal in terms of performance like signal to noise ratio (SNR), correlation coefficient (CC), mean squared error (MSE) and distortion coefficients (THD). As results, different types of thresholding and mother wavelets were in consideration and it was revealed that Daubechies along with the soft thresholding technique and four level of decomposition are better for a higher SNR and correlation coefficient thus decreasing the mean squared error and distortion factor so, these results improve the validity of the proposed technique.