Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network

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.

Authors:

• V Krishnaveni
• S Jayaraman
• A Gunasekaran
• K Ramadoss

Keywords:

• Auto Regressive (AR) Coefficients
• Feed Forward Neural Network (FNN)
• Joint Approximation Diagonalisation of Eigen matrices (JADE) Algorithm
• Polynomial Neural Network (PNN).

References:

[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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 .
[6] 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.
[7] 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
[8] 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
[9] 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
[10] 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.
[11] Vitaly Schetinin Theorie Labor, Friedrich-Schiller, "Polynomial Neural
Networks Learnt to Classify EEG Signals," NIMIA-SC October 2001.
[12] 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.
[13] 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.
[14] Nalimov V.C. and Chernova N. A., "Statistical methods of planning the
extremum experiments, Moscow, 1965.
[15] Ivakheneko A G, "Polynomial Theory of Complex System, IEEE
Transactions on Systems, Man, and Cybernetics, SMC-1(4), 1971, pp
364-378.
[16] Farlow, S.J., "Self-organizing Methods in Modeling: GMDH Type
Algorithms", Marcel Dekker, New York, 1984.
[17] Chang, F. J. and Hwang, Y.Y., "A self-organization algorithm for realtime
flood forecast, Hydrological processes", 13, 1999, pp 123-138.
[18] H.R. Madala, A.G. Ivakhnenko. "Inductive Learning Algorithms for
Complex Systems Modeling," 1994
[19] 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.
[20] http://cse.stanford.edu/class/sophomorecollege/projects00/neural/networ
ks/Architecture /feedforward/html
[21] 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.
[22] 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.
[23] Burg, J.P., "A new analysis technique for time series data," NATO Adv.
Study Inst. Signal Processing With Emphasis on Underwater Acoust.,
Aug. 1968
[24] Monson H Hayes, "Statistical Digital Signal Processing and Modeling"
John Wiley & Sons, Inc, 1996
[25] 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.
[26] http://www.sccn.ucsd.edu/~arno/famzdata/publicly_available_EEG_data
.html
[27] Sivanandam S.N. Paulraj M., "Introduction to artificial neural
networks," Vikas publishers, 2004, pp. 39-41.
[28] 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.
[29] Andreas Jung, Dissertation on "Statistical analysis of biomedical data"
University of Regensburg, 2003.