Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks

Shams Shamsabad Farahani, Shoorangiz; Arefi, Mohammad Mahdi; Zaeri, Amir Hossein

doi:10.29252/mjee.14.4.133

Document Type : Review Article

Authors

¹ 1- Department of Electrical Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran

² Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, 71348-51154 Shiraz, Iran.

³ Department of Electrical Engineering, Shahin shahr Branch, Islamic Azad University, Shahin shahr, Iran

https://doi.org/10.29252/mjee.14.4.133

Abstract

Electroencephalography (EEG) is a major clinical tool to diagnose, monitor and manage neurological disorders which is mostly affected by artifacts. Given the importance and the need for an automated method to remove artifacts, in this paper some intelligent automated methods are proposed which are composed of three main parts as extraction of effective input, filtering and filter optimization. Wavelet transform is utilized to extract the effective input, and the wavelet approximation coefficients are used as an effective input signal. In addition, Radial Basis Function Neural Network (RBFNN) has been used for filtering. The appropriate number of RBFs has been selected using extensive simulations, and the optimal value of spread parameter has been achieved by Bees algorithm (BA). Finally, the proposed artifact removal schemes have been evaluated on some real contaminated EEG signals in Mashad Ghaem hospital database. The results show that the proposed artifact removal schemes are able to effectively remove artifacts from EEG signals with little underlying brain signal distortion.

Keywords

References

[1] S. Sanei, JA. Chambers, “EEG Signal processing”, John Wiley & Sons, 2008.

[2] L. Sornmo, P. Laguna, “Bioelectrical signal processing in cardiac and neurological applications”, Academic Press, 2005.

[3] J.R. Ives, D.L. Schomer, “A 6-pole filter for improving the readability of muscle contaminated EEGs,” ELECTROEN CLIN NEURO, Vol. 69, pp. 486-490, 1988.

[4] W. Zhou, J. Gotman, “Removing eye-movement artifacts from the EEG during the intracarotid amobarbital procedure,” Epilepsia, Vol. 46, pp. 409-414, 2005.

[5] R.J. Croft, R.J. Barry, “Removal of ocular artifact from the EEG: a review,” NEUROPHYSIOL CLIN, Vol. 30, pp.5-19, 2000.

[6] S. Choi, A. Cichocki, H.M. Park, S.Y. Lee, “Blind source separation and ICA techniques: a review,” Neural Information Proc essing-Letters and Reviews, Vol. 6, pp. 1-57, 2005.

[7] G. Junfeng, L. Pan, Y. Yong, W. Pei, “Automatic removal of eye-blink artifacts based on ICA and peak detection algorithm,” presented at the 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR), 2010.

[8] L. Pengpai, C. Zhenxin, H. Yongmei, “A method for automatic removal of EOG artifacts from EEG based on ICA-EMD,” presented at the Chinese Automation Congress (CAC), 2017.

[9] A. Jafarifarmand, M.A. Badamchizadeh, S. Khanmohammadi, M.A. Nazari, B. Mozaffari Tazehkand, “Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach,” Biomed. Signal Process. Control Vol. 31, pp.199-210, 2017.

[10] P.A. Babu, K.V. Prasad, “Removal of Ocular Artifacts from EEG Signals Using Adaptive Threshold PCA and Wavelet Transforms,” presented at the International Conference on Communication Systems and Network Technologies (IEEE 2011), pp. 572 – 575, 2011.

[11] S. Hu , M. Stead , G.A. Worrell, “Removal of Scalp Reference Signal and Line Noise for Intracranial EEGs,” presented at IEEE International Conference on Networking, Sensing and Control (IEEE 2008), pp. 1486 – 1491, 2008.

[12] M. Browne, T.R.H. Cutmore, “Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising,” CLIN NEUROPHYSIOL, Vol. 113, pp. 1403-1411, 2002.

[13] N.P. Castellanos, V.A. Makarov, “Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis,” J NEUROSCI METH, Vol. 158, pp. 300-312, 2006.

[14] S. Chong Yeh, M. Norrima, A. Hamzah, C. Paul, I. Masahiro, “Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA,” presented at IEEE Journal of Biomedical and Health Informatics, Vol. 22, pp. 664-670, 2018.

[15] M.Li, Y. Cui, J. Yang, “Automatic removal of ocular artifact from EEG with DWT and ICA Method,” Applied Mathematics & Information Sciences, Vol. 7, pp. 809-816, 2013.

[16] G.L. Wallstrom, “Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods,” RADIOTHER ONCOL, Vol. 53, pp.105-119, 2004.

[17] M. Fatourechi, A. Bashashati, R.K. Ward, G.E. Birch, “EMG and EOG artifacts in brain computer interface systems: A survey,” CLIN NEUROPHYSIOL, Vol.118, pp. 480-494, 2007.

[18] Z. Li, MJ. Er, “An online self-improved fuzzy filter and its applications,” presented at the 2nd WSEAS International Conference on Electronics, Control and Signal Processing, 2003.

[19] H. Peng, B. Hu, Q. Shi, M. Ratcliffe, Q. Zhao, Y. Qi, G. Gao, “Removal of Ocular Artifacts in EEG-An Improved Approach Combining DWT and ANC for Portable Applications,” IEEE J. Biomed. Health. Inf. Vol.17, pp. 600 – 607, 2013.

[20] P. He, G. Wilson, C. Russell, M. Gerschutz, “Removal of ocular artifacts from the EEG: A comparison between time-domain regression method and adaptive filtering method using simulated data,” MED BIOL ENG COMPUT Vol. 45,pp. 495-503, 2007.

[21] X. Navarro, F. Poree, A. Beuchee, G. Carrault, “Denoising preterm EEG by signal decomposition and adaptive filtering: A comparative study,” MED ENG PHYS, Vol.37, pp. 315–320, 2015.

[22] YP. Tan, KH. Yap, L. Wang, “Intelligent Multimedia Processing with Soft Computing”, Springer-Verlag Heidelberg, 2004.

[23] V. Krishnaveni, S. Jayaraman, A. Gunasekaran, K. Ramadoss, “Automatic Removal of Ocular Artifacts using JADE Algorithm and Neural Network,” Int. J. Biomed. Sci, Vol.2, pp. 322-333, 2009.

[24] S. Selvan, R. Srinivasan, “Removal of ocular artifacts from EEG using an efficient neural network based adaptive filtering technique,” IEEE SIGNAL PROC LET, Vol. 6, pp. 330 – 332, 1999.

[25] RJ. Howlett, CJ. Lakhmi, “Radial Basis Function Networks, Recent Developments in Theory and Applications”, Springer, 2001.

[26] SS. Patil, MK. Pawar, “Quality advancement of EEG by wavelet denoising for biomedical analysis,” presented at the International Conference on Communication, Information and Computing Technology, ICCICT 2012.

[27] A. Federico, GH. Kaufmann, “Wavelet Transform,” Vol. 34, pp. 2336–2338, 2009.

[28] DT. Pham, A. Ghanbarzadeh, E. Koç, S. Otri, S. Rahim, M. Zaidi, “The Bees Algorithm - A Novel Tool for Complex Optimisation Problems,” presented at the 2nd Virtual International Conference on Intelligent Production Machines and Systems, pp. 454-459, 2006.

Majlesi Journal of Electrical Engineering

Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks

References

References

Volume 14, Issue 4 - Serial Number 4
December 2020
Pages 133-144

Electroencephalography Artifact Removal using Optimized Radial Basis Function Neural Networks

References

References

Volume 14, Issue 4 - Serial Number 4December 2020Pages 133-144

Volume 14, Issue 4 - Serial Number 4
December 2020
Pages 133-144