Eeg Signal Enhancement using DWT

Abstract

The Encephalogram Signal (Eeg), Which Provides Essential Information On Various Brain Behaviors Is An Anatomical Non-Stationary Signal. Encephalogram Analyzes Are Useful For The Treatment Of Neurological Diseases Such As Encephalopathy, Cancers, And Many Other Injury Issues. Eeg Impulses Are Observed And Analyzed Using Electrodes With A Typically Very Minute Frequency On The Scalp, Rendering The Processing And Collecting The Data From That Signal Very Challenging. Due To The Introduction Of Objects Like Powerline Interference, Different Muscle Movements, Blinkers, Eye Movement, Heartbeat, And Breathing, The Eeg Signal Is Difficult To Analyze. Correctional Infection Treatment Requires A Thorough Examination Of Encephalograms. Denoising Issues Are Somehow Diverse Because They Are Focused On Signal Types And Sounds And Because Of Their Shrunk Features The Distinct Wavelet Gives An Effective Solution For Denouncing Non-Stationary Signals Such As Eeg. This Paper Describes The Distorted Eeg Signal With Three Completely Different Wt Strategies Such As Dwt, And Two Specific Thresholding Methods, Such As Hard Thresholds And Weak Thresholds. Compared With Roots Mean Square Error (Rmse) And Signal To Noise Ratio (Snr), The Output Of These Approaches Is Comparable.