Abstract

Electroencephalographic (EEG) signal records the electrical activity of the neurons near the scalp within the brain. Significant artifacts are introduced in the recording of EEG signals which leads to unreliable results. EEG paves the way for diagnosis of many neurological disorders and other abnormalities in the human body. The main aim of research is to get clean EEG signal with enhanced accuracy for proper diagnosis. Extensive research has been conducted in this area with different techniques. This paper reviews some of the important artifact removal techniques for performance enhancement.

References

• Niedermeyer E. and Da Silva F.L, “Electroencephalography: Basic Principles, Clinical Applications, and Related Fields”, Lippincott Williams & Wilkins. 2004
• Nunez PL, Srinivasan R., “Electric fields of the brain: The neurophysics of EEG”, Oxford University Press. 1981
• J. Anderson, “Cognitive Psychology and Its Implications”, 6th Ed., 2005, Worth Publishers, New York, NY, 17 pp
• Hamalainen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993). "Magnetoencphalography - Theory, instrumentation, and applications to noninvasive studies of the working human brain". Reviews of Modern Physics 65: 413–497.
• Blankertz, B., Curio, G., Muller, K.-R. (2002), Classifying Single Trial EEG: Towards Brain Computer Interfacing, Advances in Neural Information Proc. Syst. 14, eds. T.G. Dietterich, S. Becker and Z. Ghahramani, MIT Press: Cambridge, MA, 157-164.
• Dornhege, G., Blankertz, B., Curio, G., Muller, K.-R., “Boosting Bit Rates in Noninvasive EEG Single-Trial Classifications by Feature Combination and Multi-class Paradigms”, IEEE Trans. On biomedical Engineering, 51, 6, 993-1002 (2004).
• R. R. Harrison and C. Charles, “A low-power low-noise CMOS amplifier for neural recording applications,” IEEE Journal of Solid-State Circuits, vol. 38, no. 6, pp 958-965, June 2003.
• D C Yates and E. Villegas-Rodriguez, “An ultra low power low noise chopper amplifier for wireless EEG,” Invited Paper, 49th IEEE Midwest Symposium on Circuits and Systems, Suan Juan, Puerto Rico, Aug 6-9, 2006
• G. Antoniol and P. Tonella, “EEG data recording techniques,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 2, pp. 105114, 1997.
• J. Cardenas-Barrera, J. Lorenzo-Ginori, and E. Rodriguez-Valdivia, “A wavelet-packets based algorithm for EEG signal analysis” Medical Informatics and the Internet in Medicine, vol. 29, no. 1, pp. 1527, 2004.
• D.Gopikrishna and Anamitra Makur, “A high performance scheme for EEG Recording using a multichannel model,” Lecture Notes in Computer Science, Volume 2552/2002, pp 443-451, 2002.
• Saeid Sanei, Jonathon Chambers EEG signal processing, John Wiley & Sons Ltd, 2007
• [C. D. Binnie and H. Stefan, “Modern electroencephalography: Its role in epilepsy management,” Clinical Neurophysiology, vol. 110, pp. 1671-1697, 1999.
• Vidal, J. J., ‘Direct brain–computer communication’, Ann. Rev. Biophys. Bioengng, 2, 1973, 157–158.
• Lebedev, M. A., and Nicolelis, M. A. L., ‘Brain–machine interfaces: past, present and future’, Trendsin Neurosci., 29(9), 2006.
• J. R. Wolpaw, McFarland, D.J., Vaughan, T.M. and Schalk, G., "The Wadsworth Center Brain-Computer Interface (BCI) Research and Development Program." IEEE Transactions on Neural Systems & Rehabilitation Engineering”, vol. 11, pp. 204-207, 2003.
• S. Lemm, B. Blankertz, G. Curio, and K.-R. Muller. "Spatio-spectral filters for improved classification of single trial EEG." IEEE Transactions on. Biomedical Engineering, vol. 52, pp. 1541-1548, 2005.
• M. D. Serruya, N. G. Hatsopoulos, L. Paninski, M. R. Fellows, and J.Donoghue, “Instant neural control of a movement signal,” Nature, vol. 416, pp. 141–142, 2002.
• D. M. Taylor, S. I.H. Tillery, and A. B. Schwartz, “Direct cortical control of 3D neuroprosthetic devices,” Science, vol. 296, pp. 1829–1832, 2002.
• M. A. L. Nicolelis, “Brain-machine interfaces to restore motor function and probe neural circuits,” Nature Rev. Neurosci., vol. 4, pp. 417–422, 2003.
• J. R. Wolpaw, D. J. McFarland, and T. M. Vaughan, “Brain-computer interface research at the Wadsworth center,” IEEE Trans. Rehab. Eng., vol. 8, pp. 222–226, June 2000
• Sabbir Ibn Arman, Arif Ahmed, and Anas Syed “Cost-Effective EEG Signal Acquisition and Recording System” International Journal of Bioscience, Biochemistry and Bioinformatics, Vol. 2, No. 5, September 2012
• Aruna Tyagi, Sunil Semwal ,Gautam Shah “A Review of EEG Sensors used for Data Acquisition” National Conference on Future Aspects of Artificial intelligence in Industrial Automation (NCFAAIIA 2012) Proceedings published by International Journal of Computer Applications® (IJCA).
• Jerald Yoo, Member, IEEE, Long Yan, Member, IEEE, Dinab El Damak, Student Member, IEEE, Muhammad Awais Bin Altaf, Student Member, IEEE, Ali H. Shoeb, and Anantha P. Chandrakasan, Fellow, IEEE, An 8-channel scalable EEG acquisition SOC with fully integrated patient-specific seizure classification and recording processor Solid-State Circuits Conference Digest of Technical Papers (ISSCC), 2012 IEEE International, IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 48, NO. 1, JANUARY 2013
• Filipe, S. Dept. DTBS, CEA, Grenoble, France , A wireless multichannel EEG recording platform Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, Aug. 30 2011-Sept. 3 2011
• A Garces Correa, E Lacier, H D Patino, M E Valentinuzzi “Artifact removal from EEG signals using adaptive filters in cascade” 16th Argentine Bioengineering Congress and the 5th Conference of Clinical Engineering IOP Publishing Journal of Physics: Conference Series 90 (2007) 012081 doi:10.1088/1742-6596/90/1/012081
• V.Krishnaveni, S.Jayaraman, S.Aravind, V.Hariharasudhan, K.Ramadoss “Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform” MEASUREMENT SCIENCE REVIEW, Volume 6, Section 2, No. 4, 2006
• Vojkan Mihajlovic, Shrishail Patki, Bernard Grundlehner “The Impact of Head Movements on EEG and Contact Impedance: An Adaptive Filtering Solution for Motion Artifact Reduction”, 978-1-4244-7929-0/14/$26.00 ©2014 IEEE
• Mohammad Shahbakhti, Mohammadreza Bavi, Mehdi Eslamizadeh “Elimination of Blink from EEG by Adaptive Filtering Without Using Artifact Reference”, 2013 4th International Conference on Intelligent Systems, Modeling and Simulation
• Hong Peng, Bin Hu, Qiuxia Shi, Martyn Ratcliffe, Qinglin Zhao, Yanbing Qi, and Guoping Gao “Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 3, MAY 2013
• Chaolin Teng, Yanyan Zhang, Gang Wang Member IEEE “The Removal of EMG Artifact from EEG Signals by the Multivariate Empirical Mode Decomposition” 978-1-4799-5274-8/14/$31.00 © 20l4 IEEE
• Zhang Chaozhu, Lian Siyao, Ahmed Kareem Abdullah “A New Blind Source Separation Method to Remove Artifact in EEG Signals”, 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control
• Qinglin Zhao, Bin Hu, Yujun Shi, Yang Li, Philip Moore, Minghou Sun, and Hong Peng “Automatic Identification and Removal of Ocular Artifacts in EEG-Improved Adaptive Predictor Filtering for Portable Applications”, IEEE TRANSACTIONS ON NANOBIOSCIENCE, VOL. 13, NO. 2, JUNE 2014
• S. Femilin Sheniha, S. Suja Priyadharsini, S. Edward Rajan “Removal of Artifact from EEG Signal using Differential Evolution Algorithm”, International conference on Communication and Signal Processing, April 3-5, 2013, India
• Markus Waser, Heinrich Garn, Senior Member, IEEE “Removing Cardiac Interference from the Electroencephalogram Using a Modified Pan-Tompkins Algorithm and Linear Regression” 35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013
• Nguyen Thi Anh-Dao, Tran Duc-Nghia, Nguyen Thi-Hao, Tran Duc-Tan and Nguyen Linh-Trung “An Effective Procedure for Reducing EOG and EMG Artifacts from EEG Signals”, The 2013 International Conference on Advanced Technologies for Communications (ATC'13)
• Banghua Yang, Liangfei HE “Removal of Ocular Artifacts from EEG Signals Using ICA-RLS in BCI” 2014 IEEE Workshop on Electronics, Computer and Applications
• H. N. Suresh, C. Puttamadappa “Removal OF EMG and ECG artifacts from EEG based on real time recurrent learning algorithm”, International Journal of Physical Sciences Vol. 3 (5), pp. 120-125, May, 2008 Available online at http://www.academicjournals.org/ IJPS ISSN 1992 - 1950 © 2008 Academic Journals
• Charvi A. Majmudar, Ruhi Mahajan, and Bashir I Morshed “Real-Time Hybrid Ocular Artifact Detection and Removal for Single Channel EEG” 978-1-4799-8802-0/15/$31.00 ©2015 IEEE
• Manish N.Tibdewal, R. R. Fate, Mahadevappa, Ajoy Kumar Ray “Detection and Classification of Eye Blink Artifact in Electroencephalogram through Discrete Wavelet Transform and Neural Network“International Conference on Pervasive Computing (ICPC)-2015
• Vandana Roy, Shailaja Shukla “A NLMS Based Approach for Artifacts Removal in Multichannel EEG Signals with ICA and Double Density Wavelet Transform” 2015 Fifth International Conference on Communication Systems and Network Technologies