An improved algorithm for efficient ocular artifact suppression from frontal EEG electrodes using VMD

• Autorzy: Dora Chinmayee , Biswal Pradyut Kumar

Identyfikatory

DOI

10.1016/j.bbe.2019.03.002

• Języki publikacji: EN

Abstrakty

EN

The Electroencephalogram (EEG) recordings from the frontal lobe of the human brain help in analyzing several important brain functions like motor functions, problem-solving skills, etc. or brain disorders. These recordings are often contaminated by high amplitude and long duration ocular artifacts (OAs) like eye blinks, flutters and lateral eye movements (LEMs), hence corrupting a considerable segment of EEG. In this study, an enhanced version of signal decomposition scheme i.e. Variational Mode Decomposition (VMD) based algorithm is used for suppression of OAs. The signal decomposition is preceded by identification of ocular artifact corrupted segment using Multiscale modified sample entropy (mMSE). The band limited intrinsic mode functions (BLIMFs) are obtained using predefined K (number of required BLIMFs) and α (balancing parameter). These parameters help to detrend the EEG segment in yielding the low frequency and high amplitude BLIMFs related to OA efficiently. Upon identifying OA components from the BLIMFs and estimating OA, it is regressed with the contaminated EEG to obtain the clean EEG. The proposed VMD based algorithm provides an improved performance in comparison with the existing single channel algorithms based on Empirical mode decomposition (EMD) and Ensembled EMD (EEMD) and multi-channel algorithms like Independent component analysis (ICA) and wavelet enhanced ICA for artifact suppression and is also able to overcome their limitations. The significance of the algorithm are: (1) no additional reference EOG channel requirement, (2) OA artifact based thresholds for identification and estimation from the mode functions obtained using VMD, and (3) also address the flutter artifacts.

Słowa kluczowe

EN

electroencephalogram; EOG; artifacts; EEMD; variational mode decomposition; regression

PL

elektroencefalogram; badanie EOG; artefakt; EEMD

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 148--161
• Opis fizyczny: Bibliogr. 50 poz., rys., tab., wykr.

Twórcy

autor

Dora Chinmayee

• Dept. of Electronics & Telecommunication Engineering, International Institute of Information Technology, Bhubaneswar, India

autor

Biswal Pradyut Kumar

• Dept.of Electronics & Telecommunication Engineering, International Institute of Information Technology, Bhubaneswar, India

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).