Artifact Removal in EEG Recordings
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As EEG recordings are generally noisy, artifact removal is crucial for data preprocessing. It has long been a grand research challenge for an approach which is able (1) to remove the artifacts and (2) to avoid loss or disruption of the structural information at the same time; thus the risk of introducing bias to data interpretation may be minimized. In this study, an approach (namely, EEMD-ICA) was proposed to first decompose multivariate neural data that are possibly noisy into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD). Independent component analysis (ICA) was then applied to the IMFs to separate the artifactual components. The approach was tested against the classical ICA and the automatic wavelet ICA (AWICA) methods, which were dominant methods for artifact rejection. In order to evaluate the effectiveness of the proposed approach, experiments were firstly performed using semi-simulated data mixed with a variety of noises. Results indicate that the proposed approach continuously outperforms the counterparts, and the superiority becomes even greater with the decrease of SNR in all cases. To further examine the potentials of the approach in sophisticated applications, the approach together with the counterparts was used to preprocess a real-life epileptic EEG with absence of seizure. Experiments were carried out to distinguish seizure states after artifact rejection. Using multi-scale permutation entropy to extract feature and linear discriminant analysis for classification, the EEMD-ICA performed the best for classifying the states (87.4 %, about 4.1 % and 8.7 % higher than that of AWICA and ICA, respectively), which was closest to the results of the manually selected dataset (89.7 %).
KeywordsEEG Artifact rejection Ensemble empirical mode decomposition (EEMD) Independent component analysis (ICA)
- Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett. 2002;88(17).Google Scholar
- Cassani R, Falk TH, Fraga FJ, Kanda PA, Anghinah Renato. The effects of automated artifact removal algorithms on electroencephalography – based Alzheimer’s disease diagnosis. Front Aging Neurosci. 2014;6:55.Google Scholar
- Comon P, Jutten C. Handbook of blind source separation. London: Academic Press; 2010.Google Scholar
- Zeng K, Chen D, Ouyang G, Wang L, Liu X, Li X. An EEMD-ICA approach to enhancing artifact rejection for noisy multivariate neural data. IEEE Transact Neural Syst Rehabil Eng. 2014;24(6):630–8.Google Scholar