Electroencephalogram-Artifact Extraction Enhancement Based on Artificial Intelligence Technique

Abstract:

Blind source separation (BSS) is an important technique used to recover isolated independent sources signals from mixtures. This paper proposes two blind artificial intelligent separation algorithms based on hybridization between artificial intelligent techniques with classical blind source separation algorithms to enhance the separation process. Speedy genetic algorithm SGA directly guesses the optimal coefficients of the separating matrix based on candidate initial from classical BSS algorithms also the separation criteria based on minimization of mutual information between the separating independent components. The proposed algorithms are tested by real Electroencephalogram (EEG) data, the experimental results indicate that the algorithms can quickly and effectively get optimum solution to linear blind source separation compared to classical BSS techniques, the proposed works are described by high accuracy and robustness.

• Periodical: Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 27)
• Pages: 77-91
• DOI: https://doi.org/10.4028/www.scientific.net/JBBBE.27.77
• Online since: May 2016
• Authors: Chao Zhu Zhang, Ahmed Kareem Abdullah*, Ali Abdullabs Abdullah
• Keywords: Artifact Extraction, Artificial Intelligence Techniques, Blind Source Separation (BSS), Electroencephalogram (EEG), Ocular Artifact (OA), Power Line Noise Interference

References

[1] M. Teplan, Fundamentals of EEG Measurement, measurement science review, vol. 2, (2002).

[2] M. D. Mileros, A Real-Time Classification Approach of a Human Brain-Computer Interface Based on Movement Related Electroencephalogram, Department of physics and Electrical Engineering, Linköping University, Linköping, Sweden, (2003).

[3] J. Lehtonen, EEG-Based Brain Computer Interfaces, MSc., Electrical and communication Engineering Department, University of Technology, Helsinki, (2002).

[4] H. H. Jasper, The ten-twenty electrode system of the International Federation. Electroencephalogr., Clin. Neurophysiol, vol. 10, p.371–375, (1958).

[5] A. K. Abdullah, Z. C. Zhu, L. Siyao, and S. M. Hussein, Blind Source Separation Techniques Based Eye Blinks Rejection in EEG Signals, Information Technology Journal, vol. 13, pp.401-413, (2014).

DOI: 10.3923/itj.2014.401.413

[6] R. Tahir, Constrained Blind Source Separation of Human Brain Signals, PhD Thesis, Department of Computer Engineering, Kyung Hee University, Seoul, Korea, (2010).

[7] M. Zibulevsky and M. Elad., L1–L2 optimization in signal and image processing, IEEE Signal Processing Magazine, vol. 27, p.76–81, (2010).

DOI: 10.1109/msp.2010.936023

[8] Y. Xian-chuan, X. Jin-dong, H. Dan, and X. Hai-hua, A new blind image source separation algorithm based on feedback sparse component analysis, Signal Processing, vol. 93, p.288–296, (2013).

DOI: 10.1016/j.sigpro.2012.08.010

[9] P. Comon and C. Jutten, Handbook of Blind Source Separation: Inde pendent Component Analysis and Applications, Academic Press is an Imprint of Elsevier, p.779–814, (2010).

[10] T. Rasheed, Constrained Blind Source Separation of Human Brain Signals, PhD Thesis, Department of Computer Engineering, Kyung Hee University, Seoul, Korea, (2010).

[11] G. Gratton, M. G. H. Coles, and E. Donchin, A New Method for off line Removal of Ocular Artifacts, Electroencephalography & Clinical Neurophysiology, vol. 55, pp.468-484, (1989).

DOI: 10.1016/0013-4694(83)90135-9

[12] S. Makeig, T. -P. Jung, A. J. Bell, and T. J. Sejnowski., Independent Component Analysis of Electroencephalographic data, in Advances in Neural Information Processing Systems. vol. 8, ed Cambridge MA: MIT Press, 1996, pp.145-151.

[13] A. J. Bell and T. J. Sejnowski, An information-maximisation approach to blind separation and blind deconvolution, Neural Computation, vol. 7, pp.1004-1034, (1995).

DOI: 10.1162/neco.1995.7.6.1129

[14] H. A and O. E, A Fast fixed-point algorithm for independent component analysis , Neural Comput., p.1483–92, (1997).

[15] K. S. Vijilal, P. Kanagasabapathy, S. Johnson, and V. Ewards, Artifacts Removal in EEG Signal using Adaptive Neuro Fuzzy Inference System, IEEE - ICSCN 2007, MIT Campus, Anna University, Chennai, India, pp.589-591, (2007).

DOI: 10.1109/icscn.2007.350676

[16] W. Yu, L. Zhenxing, and L. ChangHai, Improved Particle Swarm to Nonlinear Blind Source Separation, in IEEE 2007 International Symposium on Microwave, Antenna, Propagation, and EMC Technologies For Wireless Communications, Hangzhou. China, 2007, pp.1128-1133.

DOI: 10.1109/mape.2007.4393468

[17] A. K. Abdullah, C. Z. Zhang, and Y. Cang, A novel evolutionary approach for blind source separation based on stone's method., in Proceeding of the 11th International Conference on Signal Processing, Beijing, China, 2012, pp.324-328.

[18] T. -P. Jung, C. Humphries, T. W. Lee, M. J. McKeown, V. Iragui, S. Makeig, et al., Removing electroencephalographic artifacts by blind source separation, Psychophysiology, vol. 37, pp.163-178, (2000).

DOI: 10.1111/1469-8986.3720163

[19] R. Dhiman, J. S. Saini, Priyanka, and A. P. Mittal, Artifact removal from EEG recordings – An overview, presented at the NCCI 2010 -National Conference on Computational Instrumentation, CSIO Chandigarh, INDIA., (2010).

[20] V. S. Krishnaveni, A. Jayaraman, Gunasekaran, and K. Ramadoss, Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network, International Journal of Biological and Life Sciences, vol. 2, pp.10-21, (2006).

[21] D. A. Overton and C. Shagass, Distribution of eye movement and eye blink potentials over the scalp, Electroencephalography and Clinical Neurophysiology, vol. 27, p.546, (1969).

[22] S. Javidi, D. P. Mandic, C. C. Took, and A. Cichocki, Kurtosis-based blind source extraction of complex non-circular signals with application in EEG artifact removal in real-time, frontiers in neuroscience, vol. 5, (2011).

DOI: 10.3389/fnins.2011.00105

[23] C. Jutten and J. Karhunen, Advances in Blind Source Separation (BSS) and Independent Component Analysis (ICA) for Nonlinear Mixtures, International Journal of Neural Systems, vol. 14, pp.267-292, (2004).

DOI: 10.1142/s012906570400208x

[24] C. Jutten, M. Babaie-Zadeh, and S. Hosseini, Three Easy Ways for Separating Nonlinear Mixtures?, Signal Processing, vol. 84, pp.217-229, (2004).

DOI: 10.1016/j.sigpro.2003.10.011

[25] A. Taleb and C. Jutten, Source separation in post-nonlinear mixtures, IEEE Trans. On Signal Processing, vol. 47, pp.2807-2820, (1999).

DOI: 10.1109/78.790661

[26] S. M. Hussein, Design and Implementation of AI Controller Based on Brain Computer Interface, Master, Computer Engineering, Nahrain University, Iraq-Baghdad, (2007).

[27] A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. United States: J. Wiley New York, (2001).

[28] T. D. Lagerlund, F. W. Sharbrough, and N. E. Busacker, Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition, J. Clin. Neurophysiol., vol. 14, pp.73-82, (1997).

DOI: 10.1097/00004691-199701000-00007

[29] E. A.P., Computational Intelligence: John Wiley and Sons, (2002).

[30] N. Pillay, A Particle Swarm Optimization Approach For Tuning Of Siso Pid Control Loops , Master, Department of Electronic Engineering., Durban University of Technology., (2008).

DOI: 10.51415/10321/488

[31] H. R., C. B., d. W. O., and V. G., A Comparison of Particle Swarm Optimization and the Genetic Algorithm, presented at the Structural Dynamics & Materials Conference, American Institute of Aeronautics and Astronautics, (2005).

[32] D. M. and G. L.M., Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation, pp.53-66, (1997).

DOI: 10.1109/4235.585892

[33] G. Kasilingam, Particle Swarm Optimization Based PID power system Stabilizer for a Synchronous Machine, International Journal of Electrical, Robotics, Electronics and Communications Engineering, vol. 8, (2014).

[34] M. S. Rahimian and K. Raahemifar, Optimal PID Controller Design For Avr System Using Particle Swarm Optimization Algorithm, presented at the Electrical and Computer Engineering (CCECE), 2011 24th Canadian Conference on, Niagara Falls, ON, (2011).

DOI: 10.1109/ccece.2011.6030468

[35] A. T. Al-Awami, Y. L. Abdel-Magid, and M. A. Abido, A particleswarm-based approach of power system stability enhancement with unified power flow controller, Electric Power and Energy Systems, vol. 29, pp.251-259, (2007).

DOI: 10.1016/j.ijepes.2006.07.006

[36] A. K. Abdullah, Artifact Extraction from Human Brain-EEG Mixture Based on Modified Blind Source Separation Techniques, PhD, inforamtion and communication Harbin Engineering University (2015).

[37] F. Rojas, C. G. Puntonet, M.R. Álvarez, and I. Rojas, Evolutionary Algorithm Using Mutual Information for Independent Component Analysis, in 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN2003, Spain, 2003, pp.233-240.

DOI: 10.1007/3-540-44869-1_30

[38] M. Samira and A. Ebrahimzadeh, Blind Signal separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information, Spring Journal of Radioelectronic and communication systems, vol. 54, p.315–324, (2011).

DOI: 10.3103/s0735272711060045

[39] M. Zima, P. Tichavský, K. Paul, and V. Krajča, Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet -enhanced ICA and adaptive combining of tentative reconstructions, Physiol. Meas. , vol. 33, p. N39, (2012).

DOI: 10.1088/0967-3334/33/8/n39