HILBERT-HUANG TRANSFORM BASED HIERARCHICAL CLUSTERING FOR EEG DENOISING

Empirical mode decomposition (EMD) is a recently introduced decomposition method for non-stationary time series. The sum of the decomposed intrinsic mode functions (IMF) can be used to reconstruct the original signal. However, if the signal is corrupted by wideband additive noise, several IMFs may contain mostly noise components. Hence, it is a challenging study to determine which IMFs have informative oscillations or information free noise components. In this study, hierarchical clustering based on instantaneous frequencies (IF) of the IMFs obtained by the Hilbert-Huang Transform (HHT) is used to denoise the signal. Mean value of Euclidean distance similarity matrix is used as the threshold to determine the noisy components. The proposed method is tested on EEG signals corrupted by white Gaussian noise to show the denoising performance of the proposed method.