International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 10, No. 5, 8 May 2018
Cover page and Table of Contents: PDF (size: 1729KB)
Removal of Ocular Artifacts in Single Channel EEG by EMD, EEMD and CEEMD Methods Inspired by Wavelet Thresholding
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Author(s)
Index Terms
Electroencephalogram (EEG), Ocular artifacts, Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition (CEEMD), Thresholding
Abstract
Electroencephalogram (EEG) is a widely used signal for analyzing the activities of the brain and usually contaminated with artifacts due to movements of eye, heart, muscles and power line interference. Owing to eye movement, Ocular Activity creates significant artifacts and makes the analysis difficult. In this paper, a new threshold is presented for correction of Ocular Artifacts (OA) from EEG signal using Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) methods. Unlike the conventional EMD based EEG denoising techniques, which neglects the higher order low-frequency Intrinsic Mode Functions (IMFs), IMF Interval thresholding is opted to correct the artifacts. Obtained the noisy IMFs based on MI scores and perform interval thresholding to the noisy IMFs gives a relatively cleaner EEG signal. Extensive computations are carried out using EEG Motor Movement/Imagery (eegmmidb) dataset and compare the performance of Proposed Threshold (PT) with current threshold functions i.e., Universal Threshold (UT), Minimax Threshold (MT) and Statistical Threshold (ST) using several standard performance metrics: change in SNR (ΔSNR), Artifact Rejection Ratio (ARR), Correlation Coefficient (CC), and Root Mean Square Error (RMSE). Results of these studies reveal that CEEMD+PT is efficient to correct OAs in EEG signals and maintaining the background neural activity in non-artifact zones.
Cite This Paper
Vijayasankar. Anumala, Rajesh Kumar. Pullakura," Removal of Ocular Artifacts in Single Channel EEG by EMD, EEMD and CEEMD Methods Inspired by Wavelet Thresholding ", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.5, pp. 45-55, 2018. DOI: 10.5815/ijigsp.2018.05.05
Reference
[1]T. Zikov, S. Bibian, G. A. Dumont, M. Huzmezan, C. R. Ries. A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram. In: IEEE Engineering in Medicine and Biology, IEMBS 2002; Houston, TX, USA: 23-26 Oct 2002. pp.98-105.
[2]V. Krishnaveni, S. Jayaraman, N. Malmurugan. Non adaptive thresholding methods for correcting ocular artifacts in EEG. Acad. Open Internet Journal 2004; 13.
[3]V. Krishnaveni, S. Jayaraman, L. Anitha, K. Ramadoss. Automatic identification and removal of ocular artifacts from EEG using wavelet Transform. Measurement science review 2006; 6: 45-57.
[4]Saleha Khatun, Ruhi Mahajan. Comparative study of wavelet-based unsupervised ocular artifact removal Techniques for single-channel EEG data. IEEE Journal of Transitional Engineering in health and medicine 2016; 4.
[5]Huang N E, Shen Z, Long S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary Time Series Analysis. Proceeding of Royal Society 1998: IEEE. 454, pp.903-995, 1998.
[6]Raghavendra, D. Narayana Dutt. Correction of Ocular Artifacts in EEG Recordings using Empirical Mode Decomposition. Psychophysiology 2010; 47, pp.955-960.
[7]Mohammad Shahbakhti, Vahidreza Khalili, Golnoosh Kamaee. Removal of Blink from EEG by Empirical Mode Decomposition (EMD). 5th Biomedical Engineering International Conference (BMEiCON) 2012; Sunee Grand Hotel & Convention Center Muang, Ubon Ratchathani, Thailand. 5th -7th Dec 2012. pp. 1-5.
[8]A. Mert, A. Akan. EEG Denoising Based on Empirical Mode Decomposition and Mutual Information. IFMBE proceedings 2013; 41, pp.631-634, 2013.
[9]Zhu Xiao-jun, Lv Shi-qin, Liu-juan Fan, Xue-li Yu. The EEG Signal Process Based on EEMD. IEEE International Symposium on Intelligent Processing and Trusted Computing, Hubei, 22-23 Oct 2011; pp. 222-225.
[10]Z. Wu and N. E. Huang. Ensemble empirical mode decomposition: a noise-assisted data analysis method. In: Advances in Adaptive Data Analysis 2009; 1, 1-41.
[11]Marcelo A. Colominas, Gastón Schlotthauer, María E. Torres. Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomedical Signal Processing and Control 2014; 14, 19-29.
[12]Marıa E. Torres, Marcelo A. Colominas, Gaston Schlotthauer, Patrick Flandrin. A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. In: IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP); Prague, Czech Republic, May 2011. pp. 4144-4147.
[13]Yannis Kopsinis, Stephen McLaughlin. Development of EMD- Based Denoising Methods Inspired by Wavelet Thresholding. IEEE Transactions on Signal Processing, 2009; 57, 1351-1362.
[14]Salim Lahmiri, Mounir Boukadoum. A Weighted Bio-signal Denoising Approach Using Empirical Mode Decomposition. Biomed Eng Lett 2015; 5, pp.131-139.
[15]Kevin T Sweeney, Sean F McLoone, Tom´as E Ward. The use of Ensemble Empirical Mode Decomposition with Canonical Correlation Analysis as a Novel Artifact Removal Technique. IEEE Transactions on Biomedical Engineering 2011; 1-9.
[16]Suguru Kanoga, Yasue Mitsukura. Eye Blink Artifact Rejection in Single-Channel Electroencephalographic Signals by Complete Ensemble Empirical Mode Decomposition and Independent Component Analysis. In. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015; Milan, Italy, 25-29 Aug 2015. pp. 121-124.
[17]D.L.Donoho. Denoising by soft-thresholding. IEEE Tran. On Inf. Theory 1995; 41, 613-627.
[18]Daniel Valencia, David Orejuela, Jeferson Salazar, Comparison Analysis between Rigrsure, Sqtwolog, Heursure and Minimaxi Techniques Using Hard and Soft Thresholding Methods. In: IEEE Bucaramanga, Colombia, 31st Aug -2nd Sep 2016. pp. 1-5.
[19]Mantosh Biswas, Hari Om. A New Soft-Thresholding Image Denoising Method. 2nd International Conf on Communication, Computing & Security 2012; Rourkela, 6th -8th Oct 2012. pp. 10-15.
[20]A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark,J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: Components of A New Research Resource For Complex Physiologic Signals, Circulation 101 (June (23)), e215–e220.2000.