Practical Denoising of MEG Data using Wavelet Transform

TL;DR

This paper presents a practical wavelet-based denoising method for MEG data, improving signal clarity by reducing noise through multiresolution decomposition with various mother wavelets.

Contribution

It introduces a novel application of wavelet transform and multiresolution decomposition for effective MEG data denoising, tested with multiple mother wavelets.

Findings

Effective noise reduction demonstrated on MEG signals

Improved signal-to-noise ratio in denoised data

Versatility across different mother wavelets

Abstract

Magnetoencephalography (MEG) is an important noninvasive, nonhazardous technology for functional brain mapping, measuring the magnetic fields due to the intracellular neuronal current flow in the brain. However, the inherent level of noise in the data collection process is large enough to obscure the signal(s) of interest most often. In this paper, a practical denoising technique based on the wavelet transform and the multiresolution signal decomposition technique is presented. The proposed technique is substantiated by the application results using three different mother wavelets on the recorded MEG signal.