Comparative Study of KEM, LWR and HOYW Denoising Methods

Abstract

Background and Objective: In numerous occasions the recorded Neural signals are often contaminated by Noise from various intrinsic and extrinsic sources of the system. The noisy data can often give deceptive results when statistical analysis is performed on them. Hence, denoising the contaminated signal by filtering or otherwise is very important to get meaningful results. Several denoising techniques such as Kalman Filtering in conjunction with Expectation-Maximization (KEM algorithm), Levinson-Wiggins-Robinson (LWR) algorithm among others were developed in the recent past. This gave rise to a need for making “a comparative study on the performance of various noise removal methods” to assess their effectiveness.