Denoising and Frequency Analysis of Noninvasive Magnetoencephalography Sensor Signals for Functional Brain Mapping

TL;DR

This paper presents a wavelet-based denoising method for MEG signals to improve functional brain mapping, followed by frequency analysis to identify brain oscillation frequencies, with results demonstrated through spectrograms.

Contribution

Introduces a wavelet transform-based denoising technique for MEG signals and applies frequency analysis to enhance brain activity interpretation.

Findings

Effective noise reduction in MEG signals

Identification of major brain oscillation frequencies

Improved time-frequency visualization

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, most often, the inherent level of noise in the MEG sensor data collection process is large enough to obscure the signal(s) of interest. In this paper, a denoising technique based on the wavelet transform and the multiresolution signal decomposition technique along with thresholding is presented, substantiated by application results. Thereafter, different frequency analysis are performed on the denoised MEG signals to identify the major frequencies of the brain oscillations present in the denoised signals. Time-frequency plots (spectrograms) of the denoised signals are also provided.