Performance Improvement in Electroencephalogram Signal by Using DWT

Abstract

The nature of the electroencephalogram (EEG) signal is very fluid and spontaneous. Because of its lower amplitude, it is polluted by certain artifacts such as power line noise and baseline noise. Similarly, power line and baseline sounds have polluted electromyogram (EMG) and electrocardiogram (ECG) signals. These objects taint the initial signal's properties. Owing to the existence of objects, these signals cannot be accurately analyzed; hence, these sounds must be removed before analyzing the raw signal. The two important artifacts that corrupt a patient's EEG record are power line interference and baseline interference. This paper focuses on extracting power line and baseline artifacts from EEG signals using an effective de-noising algorithm called DWT (Discrete Wavelet Transform) to improve the signal's efficiency by increasing fidelity parameters including MSE, MAE, SNR, and PSNR. The MATLAB simulation is to be used for the implementation.