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Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network
Authors: V Krishnaveni, S Jayaraman, A Gunasekaran, K Ramadoss
Abstract:The ElectroEncephaloGram (EEG) is useful for clinical diagnosis and biomedical research. EEG signals often contain strong ElectroOculoGram (EOG) artifacts produced by eye movements and eye blinks especially in EEG recorded from frontal channels. These artifacts obscure the underlying brain activity, making its visual or automated inspection difficult. The goal of ocular artifact removal is to remove ocular artifacts from the recorded EEG, leaving the underlying background signals due to brain activity. In recent times, Independent Component Analysis (ICA) algorithms have demonstrated superior potential in obtaining the least dependent source components. In this paper, the independent components are obtained by using the JADE algorithm (best separating algorithm) and are classified into either artifact component or neural component. Neural Network is used for the classification of the obtained independent components. Neural Network requires input features that exactly represent the true character of the input signals so that the neural network could classify the signals based on those key characters that differentiate between various signals. In this work, Auto Regressive (AR) coefficients are used as the input features for classification. Two neural network approaches are used to learn classification rules from EEG data. First, a Polynomial Neural Network (PNN) trained by GMDH (Group Method of Data Handling) algorithm is used and secondly, feed-forward neural network classifier trained by a standard back-propagation algorithm is used for classification and the results show that JADE-FNN performs better than JADEPNN.
Keywords: Auto Regressive (AR) Coefficients, Feed Forward Neural Network (FNN), Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm, Polynomial Neural Network (PNN).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333386Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1740
 Lagerlund TD, Sharbrough FW, Busacker NE, "Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition", Clinical Neurophysiology, 14(1), 1997, pp 73 - 82.
 Scott Makeig, Tzyy-Ping Jung, Anthony J Bell, Terrence J Sejnowski, "Independent Component Analysis of Electroencephalographic data", Advances in Neural Information Processing Systems 8 MIT Press, Cambridge MA, Vol (8), 1996, pp 145-151.
 Tzyy-Ping Jung, Scott Makeig, Colin Humphries, Te-won Lee, Martin J Mckeown, Vincent Iragui and Terrence J Sejnowski, "Extended ICA removes Artifacts from Electroencephalographic recordings", Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, MA, 1998, pp 894-900.
 Vigario R, Jaakko Sarela, Veikko Jousmaki, Matti Hamalainen, Erkki Oja, "Independent Component Approach to the Analysis of EEG and MEG Recordings", IEEE Transactions on Biomedical Engineering, Vol 47, No.5, May 2000 pp 589-593.
 Delorme.A, Makeig.. S & Sejnowski T, "Automatic artifact rejection for EEG data using high-order statistics and independent component analysis", Proceedings of the Third International ICA Conference, 2001, pp 9-12 .
 N.Nicolaou and S.J.Nasuto, "Temporal Independent Component Analysis for automatic artefact removal from EEG", 2nd International Conference on Medical Signal and Information Processing, Malta, 2004, pp 5-8.
 Carrie A.Joyce, Irina F Gorodnitsky and Marta Kutas, "Automatic removal of eye movement and blink artifacts from EEG data using blind component separation", Psychophysiology, 41 Issue 2, 2004, pp 313- 325
 Shoker L, Sanei S and Chambers J, "Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm" IEEE Signal Process. Lett. 12, 2005 pp 721-4
 Yandong Li, Zhongwei Ma, Wenkai Lu and Yanda Li, "Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach" Physiological Measurement 27, 2006, pp 425-436
 V Krishnaveni, S Jayaraman, Chaitanya Mathi, N Malmurugan, K Ramadoss, "Quantitative Evaluation of Signal Separation Algorithms for removal of ocular artifacts from EEG", National Journal of Technology, No.2, Vol.1, 2005, pp 47-53.
 Vitaly Schetinin Theorie Labor, Friedrich-Schiller, "Polynomial Neural Networks Learnt to Classify EEG Signals," NIMIA-SC October 2001.
 S.C Satapathy, P.K. Dash, G.Panda, B.B.Mishra, "Polynomial Neural Swarm Classifier" MMU International Symposium on Information and Communication Technologies MUSIC 2004.
 X.Wang, L.Li, D.Lockington, D.Pullar, D.S.Jeng, "Self-Organizing Polynomial Neural Network for Modelling Complex Hydrological Processes" Research Report No R861, University of Sydney, December 2005.
 Nalimov V.C. and Chernova N. A., "Statistical methods of planning the extremum experiments, Moscow, 1965.
 Ivakheneko A G, "Polynomial Theory of Complex System, IEEE Transactions on Systems, Man, and Cybernetics, SMC-1(4), 1971, pp 364-378.
 Farlow, S.J., "Self-organizing Methods in Modeling: GMDH Type Algorithms", Marcel Dekker, New York, 1984.
 Chang, F. J. and Hwang, Y.Y., "A self-organization algorithm for realtime flood forecast, Hydrological processes", 13, 1999, pp 123-138.
 H.R. Madala, A.G. Ivakhnenko. "Inductive Learning Algorithms for Complex Systems Modeling," 1994
 J.A. M├╝ller, F. Lemke, A.G. Ivakhnenko., "GMDH Algorithms for Complex Systems Modeling. Mathematical and Computer Modeling of Dynamical Systems," Vol. 4, 1998, pp. 275-315.
 http://cse.stanford.edu/class/sophomorecollege/projects00/neural/networ ks/Architecture /feedforward/html
 Mark Polak Aleksandar Kostov, "Feature extraction in development of brain-computer feature extraction in development of brain-computer," Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 20, No.4 1998.
 Nai-Jen, Huan and Ramaswamy Palaniappan , "Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals," Proceedings of the 26th Annual International Conference of the IEEE EMBS, September 2004.
 Burg, J.P., "A new analysis technique for time series data," NATO Adv. Study Inst. Signal Processing With Emphasis on Underwater Acoust., Aug. 1968
 Monson H Hayes, "Statistical Digital Signal Processing and Modeling" John Wiley & Sons, Inc, 1996
 Keirn, Z.A., and Aunon, J.I, "A new mode of communication between man and his surroundings," IEEE Transactions on Biomedical Engineering, Vol. 37, No.12 December 1990, pp. 1209-1214.
 http://www.sccn.ucsd.edu/~arno/famzdata/publicly_available_EEG_data .html
 Sivanandam S.N. Paulraj M., "Introduction to artificial neural networks," Vikas publishers, 2004, pp. 39-41.
 L. Tarassenko, Y.U.Khan, M.R.G Holt, "Identification of inter-ictal spikes in the EEG using neural network analysis" Inst. Elect. Eng._Proc. Sci Meas. Technol., Vol 145, No.6, 1998, pp 270-278.
 Andreas Jung, Dissertation on "Statistical analysis of biomedical data" University of Regensburg, 2003.