**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**32586

##### Application of Mutual Information based Least dependent Component Analysis (MILCA) for Removal of Ocular Artifacts from Electroencephalogram

**Authors:**
V Krishnaveni,
S Jayaraman,
K Ramadoss

**Abstract:**

The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.

**Keywords:**
Electroencephalogram,
Ocular Artifacts (OA),
Independent Component Analysis (ICA),
Mutual Information (MI),
Mutual Information based Least dependent Component Analysis(MILCA)

**Digital Object Identifier (DOI):**
doi.org/10.5281/zenodo.1079500

**References:**

[1] Croft RJ, Barry RJ (2000) "Removal of ocular artifact from the EEG: a review" Clinical Neurophysiology, 30(1), pp 5-19.

[2] A.Kandaswamy, V Krishnaveni, .S. Jayaraman, N.Malmurugan and.K.Ramadoss (2005), "Removal of Ocula Artifacts from EEG - A Survey" IETE Journal of Research, Vol 52, No.2, March-April-2005

[3] Gratton. G, Coles MG, Donchin E (1983) "A new method for off-line removal of ocular artifact", Electroencephalography and Clinical Neurophysiology, 55(4), pp 468-484.

[4] Woestengurg JC, Verbaten MN, Slangen JL (1982), "The removal of the eye movement artifact from the EEG by regression analysis in the frequency domain" Biological Physiology, 16, pp 127-147.

[5] Lagerlund TD, Sharbrough FW, Busacker NE (1997), "Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition", Clinical Neurophysiology, 14(1), pp 73 - 82.

[6] Joliffe I T (1986), "Principal Component Analysis", Springer Verlag, New York,.

[7] Comon P. (1994), "Independent Component Analysis, A new concept", Signal Processing 36(3), pp 287-314.

[8] Scott Makeig, Tzyy-Ping Jung, Anthony J Bell, Terrence J Sejnowski (1996), "Independent Component Analysis of Electroencephalographic data", Advances in Neural Information Processing Systems 8 MIT Press, Cambridge MA, Vol (8), pp 145-151.

[9] Tzyy-Ping Jung, Scott Makeig, Colin Humphries, Te-won Lee, Martin J Mckeown, Vincent Iragui and Terrence J Sejnowski (1998), "Extended ICA removes Artifacts from Electroencephalographic recordings", Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, MA, pp 894-900.

[10] Vigario R, Jaakko Sarela, Veikko Jousmaki, Matti Hamalainen, Erkki Oja (2000), "Independent Component Approach to the Analysis of EEG and MEG Recordings", IEEE Transactions on Biomedical Engineering, Vol 47, No.5, pp 589-593.

[11] Delorme.A, Makeig.. S & Sejnowski T (2001), "Automatic artifact rejection for EEG data using high-order statistics and independent component analysis", Proceedings of the Third International ICA Conference, pp 9-12.

[12] Carrie A.Joyce, Irina F Gorodnitsky and Marta Kutas (2004), "Automatic removal of eye movement and blink artifacts from EEG data using blind component separation", Psychophysiology, Volume 41: Issue 2 , pp 313-325.

[13] N.Nicolaou and S.J.Nasuto (2004), "Temporal Independent Component Analysis for automatic artefact removal from EEG", 2nd International Conference on Medical Signal and Information Processing, Malta, pp 5-8.

[14] Alexander Kraskov, Harald Stogbauer and Peter Grassberger, "Least Dependent Component Analysis Based on Mutual Information", ArXiv: physics/0405044 vol.2 28 Sep 2004.

[15] Bell AJ, Sejnowski TJ, An information maximization approach to blind separation and blind deconvolution, Neural Computation, 7, 1995, pp 1004-1034.

[16] Lee TW and Sejnowski T, " Independent Component Analysis for Sub Gaussian and Super-Gaussian Mixtures, Proceedings of. 4th Joint Symposium on. Neural Computation, 7, 1996, pp 132-139.

[17] A. Hyv├ñrinen and E. Oja, "A fast fixed-point algorithm for independent component analysis," Neural Computation, vol. 9, 1997, pp. 1483-1492.

[18] Beloucharani, K Meriam, J F Cardoso and E Moulines, "A blind source separation technique using second order statistics", IEEE Transactions on Signal Processing, 45, Feb 1997, pp 434-444.

[19] A Ziehe and K R Muller, "TDSEP - an efficient algorithm for blind separation using time structure" in Proceedings of ICANN -98, December 1998, pp 675-680.

[20] Jean-Fran├ºois Cardoso," High-order contrasts for independent component analysis", Neural Computation, vol. 11, no 1, Jan. 1999, pp. 157ÔÇö192

[21] Juan J. Murillo-Fuentes and Rafael Boloix-Tortosa, Francisco J. Gonz'alez-Serrano, "Initialized Jacobi Optimization in Independent Component Analysis".

[22] L. Molgedey and H. Schuster, "Separation of independent signals using time-delayed correlations," Physical Review Letters, vol. 72, no. 23, pp. 3634-3637, 1994.

[23] F. R. Bach and M. I. Jordan, "Kernel independent component analysis." J. of Machine Learning Research, 3:1-48, 2002.

[24] Erik G. Miller and John W. Fisher III, "Independent components analysis by direct entropy minimization," Tech. Rep. UCB/CSD-03- 1221, University of California at Berkeley, January 2003.

[25] Alexander Kraskov, Harald Stogbauer and Peter Grassberger, "Estimating Mutual Information",. ArXiv:cond-mat/ 0305641 v1 28th May 2003

[26] Girolami.M and Fyfe C, "Extraction of independent signal sources using a deflationary exploratory projection pursuit network with lateral inhibition", IEEE proceedings on Vision Image Signal Processing, 1997, 144, (5), pp 299-306

[27] Amari.S, Cichocki.A and Yang H, "A new learning algorithm for blind signal separation" Advanced Neural Information Process. Syst. 1996, 8, pp 757-763.

[28] Cardoso, J.-F., Bose, S., & Friedlander, B. (1996), "On optimal source separation based on second and fourth order cumulants," Proc.IEEEWorkshoponSSAP. Corfu, Greece.

[29] DiMatteo, I., Genovese, C.R., Kass, R.E., 2001, "Bayesian curvefitting with free-knot splines," Biometrika 88, 1055- 1073.

[30] J. Larsen L.K. Hansen and T. Kolenda, "On independent component analysis for multimedia signals," Multimedia Image and Video Processing, CRC Press, vol. Chapter 7, pp. 175-200, 2000.

[31] B. Schölkopf and A. J. Smola, "Learning with Kernels." MIT Press, 2001.

[32] Oldrich Vasicek, "A test for normality based on sample entropy,"Journal of the Royal Statistical Society, Series B, vol. 38, no. 1, pp. 54- 59, 1976.

[33] A. Hyv├ñrinen and E. Oja, "A survey on independent component analysis," Helsinki University of Technology.

[34] V Krishnaveni, S Jayaraman, N Malmurugan, Chaitanya Mathi, K Ramadoss, "Quantitative Evaluation of Signal Seperation Algortihms for the removal of ocular artifacts from EEG" National Journal of Technology, No:2, Volume 1, June 2005 pp 47-53

[35] V Krishnaveni, S Jayaraman, P M Manoj Kumar, K Shivakumar, K Ramadoss, "Comparison of Independent Component Analysis Algorithms for removal of ocular artifacts from Electroencephalogram" Measurement Science Review Journal, Volume 5, Section 2, 2005 pp 67-79.

[36] http://www.sccn.ucsd.edu/~arno/famzdata/publicly_available_EEG_data.html