Denoising MEG sensor data using wavelets

Magnetoencephalography (MEG) is a noninvasive technology for analyzing cerebral neuronal activity. The noise level in the MEG data is large enough to affect the desired signal. This paper describes a denoising technique based on Wavelet Transform (WT). It compares denoising MEG data with different wavelet techniques like Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT) and Stationary Wavelet Transform (SWT). Here WT is implemented using Multiresolution Analysis (MRA). Spectrogram of original MEG data and its denoised version are also compared.