A new approach for ocular artifact removal from EEG signal using EEMD and SCICA

References

• Benbadis, S. R., & Rielo, D. A. (2008). EEG Artifacts Overview. http://emedicine.medscaple.com/article/1140247-overview
   Google Scholar
• Bosl, W., Tierney, A., Flusberg, H. T., & Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC Medicine, 9(1), 1–17. https://doi.org/10.1186/1741-7015-9-18
   PubMed Web of Science ®Google Scholar
• Choudhry, M. S., & Kapoor, R. (2016a, October). Removal of baseline wander from ECG using CEEMD and adaptive morphological function. Journal of Chemical and Pharmaceutical Sciences, 974-2115.
   Google Scholar
• Choudhry, M. S., & Kapoor, R. (2016b). Ocular artifact removal from EEG using stationary wavelet enhanced ICA. International Journal of Control Theory and Applications, 9(10), 4935–4945.
   Google Scholar
• Devulapalli, S. P., Rao, C. S., & Prasad, K. S. (2019, May). Eye-blink artifact removal of EEG signal using EMD. International Journal of Recent Technology and Engineering (IJRTE).
   Google Scholar
• Hagemann, D., & Naumann, E. (2001). The effects of ocular artifacts on (lateralized) broadband power in the EEG. Clinical Neurophysiology, 112 (2), 215–231. https://doi.org/10.1016/S1388-2457(00)00541-1.
   PubMed Web of Science ®Google Scholar
• Hesse, C. W., & James, C. J. (2005). The FastICA algorithm with spatial constraints. IEEE Signal Processing Letters, 12(11).
   Web of Science ®Google Scholar
• Huang, N., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., &, Liu, H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 995, 903.
   Web of Science ®Google Scholar
• Ille, N. (2001). Artifact correction in continuous recordings of the electro- and magnetoencephalogram by spatial filtering [Ph.D. dissertation].
   Google Scholar
• Inuso, G. (2007, August) Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings. In Proceedings of International Joint Conference on Neural Networks (pp. 12–17). Orlando-USA.
   Google Scholar
• Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4–5), 287–305. https://doi.org/10.1016/j.neucli.2016.07.002
   PubMed Web of Science ®Google Scholar
• Issa, M. F., & Juhasz, Z. (2019, December). Improved EOG artifact removal using wavelet enhanced independent component analysis. Journal of Brain Science, 9(12), 355. https://doi.org/10.3390/brainsci9120355
   Google Scholar
• James, C. J., & Hesse, C. W. (2006, December). On semi-blind source separation using spatial constraints with applications in EEG analysis. IEEE, Trans. On Biomed. Eng., 53(12).
   PubMed Web of Science ®Google Scholar
• Jiang, X., Bian, G.-B., & Tian, Z. (2019, February). Removal of artifacts from EEG signals: A review. Sensors.
   Web of Science ®Google Scholar
• Joyce, C. A., Gorodnitsky, I. F., & Kutas, M. (2004). Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology, 41(2), 313–325. https://doi.org/10.1111/j.1469-8986.2003.00141.x
   PubMed Web of Science ®Google Scholar
• Jung, T. P., Humphries, C., Lee, T. W., Makeig, S., McKeown, M. J., Iragui, V., & Sejnowski, J. (1998). Extended ICA removes artifacts from EEG recordings. Advances in Neural Information Processing Systems, 10, 894–900.
   Google Scholar
• Mahajan, R., & Morshed, B. I. (2015, January). Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis and wavelet-ICA. IEEE Journal of Biomedical and Health Informatics, 19(1), 158–165. https://doi.org/10.1109/JBHI.2014.2333010
   PubMed Web of Science ®Google Scholar
• Makeig, S., Bell, A. J., Jung, T. P., & Sejnowski, T. J. (1996). ICA of Electroencephalographic Data. Advances in Neural Information Processing Systems, 8, 145–151.
   Google Scholar
• Pramudita, B. A., Nugroho, Y. D., Ardiyanto, I., Shapiai, M. I., & Setiawan, N. A. (2017, November). Removing ocular artefacts in EEG signals by using combination of complete EEMD (CEEMD)- independent component analysis (ICA) based outlier data. In IEEE International Conference on Robotics, Automation and Science (ICORAS). (pp. 27–29) Malaysia.
   Google Scholar
• Rilling, G., Flandrin, P., & Es, P. (2003). On empirical mode decomposition and its algorithms. In IEEE-EURASIP Work. nonlinear signal image process (vol. 3, pp. 8–11).
   Google Scholar
• Wang, G., Teng, C., Kuo, L., Zhang, Z., & Yan, X. (2016, September). The removal of EOG artifacts from EEG signals using independent component analysis and multivariate empirical mode decomposition. IEEE Journal of Biomed. And Health Inf, 20(5).
   Web of Science ®Google Scholar
• www.physionet.org
   Google Scholar
• Zeng, K., Chen, D., Ouyang, G., Wang, L., Liu, X., & Xiaoli, L. (2016, June). An EEMD-ICA approach to enhancing artifact rejection for noisy multivariate neural data. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(6), 630–638. https://doi.org/10.1109/TNSRE.2015.2496334
   PubMed Web of Science ®Google Scholar
• Zhaohua, W., & Huang, N. E. (2005, August). Ensemble empirical mode decomposition: A noise assisted data analysis method [Thesis].
   Google Scholar