Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal

Abstract

An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process. Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures. However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity. Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis. Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band. These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients. To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures. Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysis.