Automatic minimization of ocular artifacts fromelectroencephalogram: A novel approach by combining CompleteEEMD with Adaptive Noise and Renyi’s Entropy

Ocular artifacts (OAs) are one of the major interferences that obscure electroencephalogram (EEG) signals.In this paper, a novel, completely automatic, adaptive and fast method that combines the CompleteEmpirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Renyi’s Entropy (RE) is proposed forminimizing the OAs from corrupted EEG signals. The RE criterion is suggested to automatically select theIntrinsic Mode Functions (IMFs) to reconstruct the artifact minimized EEG signals. The scheme requiresonly a single channel OAs corrupted EEG recording and a reasonable computation time. The methodis first evaluated on simulated OAs (one, two, and several blinks as well as saccadic eye movements)corrupted EEG signals and then extended to real EEG signals. The signal-to-noise ratio improvement(SNRimp) along with time and power spectral density (PSD) plots are used for evaluating the performanceof the scheme. The method is compared to the one based on the CEEMDAN and manual choice of IMFsfor OAs minimization from EEG. Results from extensive simulation studies clearly indicate the efficacyof the proposed scheme in automatically minimizing the OAs from the corrupted EEG signals.