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Artifact Removal in EEG Recordings

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Abstract

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 %).

Keywords

EEG Artifact rejection Ensemble empirical mode decomposition (EEMD) Independent component analysis (ICA) 

References

  1. Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett. 2002;88(17).Google Scholar
  2. Cai D, He XF, Han JW. SRDA: an efficient algorithm for large-scale discriminant analysis. IEEE Trans Knowl Data Eng. 2008;20(1):1–12.CrossRefGoogle Scholar
  3. Candy JV. Bayesian signal processing: classical, modern and particle filtering methods. New York: Wiley-Interscience; 2009.CrossRefGoogle Scholar
  4. 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
  5. Castellanos NP, Makarov VA. Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods. 2006;158(2):300–12.PubMedCrossRefGoogle Scholar
  6. Chen D, Li D, Xiong MZ, Bao H, Li XL. GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia. IEEE Trans Inf Technol Biomed. 2010;14(6):1417–27.PubMedCrossRefGoogle Scholar
  7. Comon P, Jutten C. Handbook of blind source separation. London: Academic Press; 2010.Google Scholar
  8. Daly I, Billinger M, Scherer R, Muller-Putz G. On the automated removal of artifacts related to head movement from the EEG. IEEE Transact Neural Syst Rehabil Eng. 2013;21(3):427–34.CrossRefGoogle Scholar
  9. Daubechies I, Lu JF, Wu HT. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal. 2011;30(2):243–61.CrossRefGoogle Scholar
  10. De Clercq W, Vergult A, Vanrumste B, Van Paesschen W, Van Huffel S. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng. 2006;53(12 Pt 1):2583–7.PubMedCrossRefGoogle Scholar
  11. Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage. 2007;34(4):1443–9.PubMedCrossRefGoogle Scholar
  12. Diniz P. Adaptive filtering: algorithms and practical implementation. New York: Springer; 2008.CrossRefGoogle Scholar
  13. Eriksson J, Koivunen V. Identifiability, separability, and uniqueness of linear ICA models. IEEE Signal Process Lett. 2004;11(7):601–4.CrossRefGoogle Scholar
  14. Fatourechi M, Bashashati A, Ward RK, Birch GE. EMG and EOG artifacts in brain computer interface systems: a survey. Clin Neurophysiol. 2007;118(3):480–94.PubMedCrossRefGoogle Scholar
  15. Fox D, Hightower J, Liao L, Schulz D. Bayesian filtering for location estimation. IEEE Pervasive Comput. 2003;2(3):24–33.CrossRefGoogle Scholar
  16. Gratton G, Coles MG, Donchin E. A new method for off-line removal of ocular artifact. Electroencephalogr Clin Neurophysiol. 1983;55(4):468–84.PubMedCrossRefGoogle Scholar
  17. Hild KE, Erdogmus D, Principe JC. An analysis of entropy estimators for blind source separation. Signal Process. 2006;86(1):182–94.CrossRefGoogle Scholar
  18. Huang NE, Shen Z, Long SR, Wu MLC, Shih HH, Zheng QN, Yen NC, Tung CC, Liu HH. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Roy Soc-Math Phys Eng Sci. 1998;454(1971):903–95.CrossRefGoogle Scholar
  19. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transact Neural Netw. 1999;10(3):626–34.CrossRefGoogle Scholar
  20. Hyvärinen A, Karhunen J, Oja E. Independent component analysis. New York: Wiley; 2001.CrossRefGoogle Scholar
  21. Ille N, Berg P, Scherg M. Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies. J Clin Neurophysiol. 2002;19(2):113–24.PubMedCrossRefGoogle Scholar
  22. Izzetoglu M, Devaraj A, Bunce S, Onaral B. Motion artifact cancellation in NIR spectroscopy using Wiener filtering. IEEE Trans Biomed Eng. 2005;52(5):934–8.PubMedCrossRefGoogle Scholar
  23. James CJ, Gibson OJ. Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Trans Biomed Eng. 2003;50:8.CrossRefGoogle Scholar
  24. Kelly JW, Siewiorek DP, Smailagic A, Collinger JL, Weber DJ, Wang W. Fully automated reduction of ocular artifacts in high-dimensional neural data. IEEE Trans Biomed Eng. 2011;58(3):598–606.PubMedCrossRefGoogle Scholar
  25. Killory BD, Bai X, Negishi M, Vega C, Spann MN, Vestal M, Guo J, Berman R, Danielson N, Trejo J, Shisler D, Novotny EJ, Constable RT, Blumenfeld H. Impaired attention and network connectivity in childhood absence epilepsy. Neuroimage. 2011;56(4):2209–17.PubMedPubMedCentralCrossRefGoogle Scholar
  26. Kopsinis Y, McLaughlin S. Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Trans Signal Process. 2009;57(4):1351–62.CrossRefGoogle Scholar
  27. Krishnaveni V, Jayaraman S, Anitha L, Ramadoss K. Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J Neural Eng. 2006;3(4):338–46.PubMedCrossRefGoogle Scholar
  28. Li XL, Cui SY, Voss LJ. Using permutation entropy to measure the electroencephalographic effects of sevoflurane. Anesthesiology. 2008;109(3):448–56.PubMedCrossRefGoogle Scholar
  29. Mammone N, La Foresta F, Morabito FC. Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sensors J. 2012;12(3):533–42.CrossRefGoogle Scholar
  30. Mijović B, Vos MD, Gligorijević I, Taelman J, Huffel SV. Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Trans Biomed Eng. 2010;57(9):2188–96.PubMedCrossRefGoogle Scholar
  31. Morbidi F, Garulli A, Prattichizzo D, Rizzo C, Rossi S. Application of kalman filter to remove TMS-induced artifacts from EEG recordings. IEEE Trans Control Syst Technol. 2008;16(6):1360–6.CrossRefGoogle Scholar
  32. Ouyang GX, Li J, Liu XZ, Li XL. Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis. Epilepsy Res. 2013;104(3):246–52.PubMedCrossRefGoogle Scholar
  33. Romero S, Mananas MA, Barbanoj MJ. A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case. Comput Biol Med. 2008;38(3):348–60.PubMedCrossRefGoogle Scholar
  34. Romero S, Mananas MA, Barbanoj MJ. Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation. Ann Biomed Eng. 2009;37(1):176–91.PubMedCrossRefGoogle Scholar
  35. Sweeney KT, McLoone SF, Ward TE. The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Trans Biomed Eng. 2013;60(1):97–105.PubMedCrossRefGoogle Scholar
  36. Wang Z, Bovik AC. Mean squared error: love It or leave It? a new look at signal fidelity measures. IEEE Signal Process Mag. 2009;26(1):98–117.CrossRefGoogle Scholar
  37. Wu ZH, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal. 2009;1(1):1–41.CrossRefGoogle Scholar
  38. 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
  39. Zeng K, Yan J, Wang Y, Sik A, Ouyang G, Li X. Automatic detection of absence seizures with compressive sensing EEG. Neurocomputing. 2016;171:497–502.CrossRefGoogle Scholar
  40. Zhang ZL, Jung TP, Makeig S, Rao BD. Compressed sensing of EEG for wireless telemonitoring with Low energy consumption and inexpensive hardware. IEEE Trans Biomed Eng. 2013;60(1):221–4.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  2. 2.Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina

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