Automatic minimization of ocular artifacts from electroencephalogram: A novel approach by combining Complete EEMD with Adaptive Noise and Renyi's Entropy. (July 2017)

• Record Type: Journal Article
• Authors: Guarascio, Mario; Puthusserypady, Sadasivan
• Is Part Of: Biomedical signal processing and control. Volume 36(2017)
• Page Start: 63
• Page End: 75
• Publication Date: 2017-07
• Subjects: Electroencephalogram (EEG) -- Ocular artifacts (OAs) -- Artifact minimization -- Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) -- Renyi's Entropy (RE); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.03.017
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: To minimize the OAs from EEG signals, a CEEMDAN-RE scheme is proposed. The proposed scheme is novel, adaptive, completely automatic and fast. The scheme needs only a single EEG channel recording and no human intervention. The high SNR improvements clearly indicate the efficacy in minimizing the OAs. Dyadic filter bank behavior could be a limitation in case of spectral overlap. Abstract: 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 Complete Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Renyi's Entropy (RE) is proposed for minimizing the OAs from corrupted EEG signals. The RE criterion is suggested to automatically select the Intrinsic Mode Functions (IMFs) to reconstruct the artifact minimized EEG signals. The scheme requires only a single channel OAs corrupted EEG recording and a reasonable computation time. The method is 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 ( SNR imp ) along with time and power spectral density (PSD) plots are used for evaluating the performance of the scheme. The method is compared to the one based on the CEEMDAN and manual choice of IMFs for OAs minimization from EEG. Results from extensive simulation studies clearly indicate the efficacy of the proposed scheme in automatically minimizing the OAs from the corrupted EEG signals.