Study and Analysis of Various Window Techniques Used in Removal of High Frequency Noise Associated in Electroencephalogram (EEG)

Abstract: EEG signals are the versatile tool for detection of various kinds of Brain activities and diseases. But when the EEG data has been recorded for analysis purpose it is contaminated by different noise signals which are caused due to power line interference, electrode movement, base line wander, muscle movement (EMG) etc. and these days the E-health care system introduces in which there is a need of transmission of signals so at the time of transmission noise introduces. These noise signals are being proved hurdles in the diagnosis of brain which is not good and also not acceptable. To avoid the problem of these noise signals there are several techniques present which are able to reduce the presence of noise in the EEG signals. This research article presents the study of commonly used various window techniques in the removal of high frequency noise associated with EEG. During the study author has analyzed four window techniques as wavelet based de-noising technique, Adaptive filter algorithm, empirical mode decomposition (EMD) based de-noising technique and thresholding techniques. There is a comparative analysis between these four de-noising techniques.