Registration of electroencephalography signals (EEG) is almost always associated with recording different kinds of artifacts that makes it difficult to read and analyze collected data. These artifacts may be noticeable in the individual channels, but very often they have to be adjusted over several channels simultaneously. Their origin can be varied. Among the most typical are network and hardware artifacts as well as several types of muscle artifacts, derived from the tested person. In recent years increased interest in EEG studies might be noticed. EEG signals are applied not only in the outpatient and clinical applications, but also in psychological analyses and in construction of modern human-machine interfaces. This article presents a case study of classification analysis application in EEG artifact correction tasks.


electroencephalogram; electroencephalography measurement; noise measurement; EEG artifacts

Barbati G., Porcaro C., Zappasodi F., Rossini P.M., Tecchio F.: Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals, Clin. Neurophysiol, 115, 2004, 1220–1232.

Barlow JS.: Artifact processing (rejection and minimization) in EEG data processing. In: Lopes da Silva FH, Storm van Leeuwan W, Remond A, editors. Handbook of electroencephalography and clinical neurophysiology. Revised series 1986; vol. 2. Amsterdam: Elsevier; 1986, 15–62.

Belouchrani A., Abed-Meraim K., Cardoso J., Moulines E.: A blind source separation technique using second-order statistics, IEEE Transactions on Signal Processing 45 (2), 1997, 434–444.

Berg P., Scherg M.: A multiple source approach to the correction of eye artifacts. Electroencephalogr. Clin.Neurophysiol. 90, 1994, 229–241.

Blinowska K., Kamiński M.: Multivariate Signal Analysis by Parametric Models. Handbook of Time Series Analysis. Björn Schelter, Matthias Winterhalder, Jens Timmer, WILEY-VCH Verlag GmbH & Co. KGaA, 2006, Weinheim.

Cichocki A., Amari S.: Adaptive Blind Signal and Image Processing Learning Algorithms and Applications, John Wiley & Sons, New York, USA, 2002.

Croft R.J., Barry R.J.: Removal of ocular artifact from the EEG: a review, Neuro physiol. Clin. 30, 2000, 5–19.

Croft R.J., Barry R.J.: EOG correction: a new perspective, Electroencephalogr. Clin. Neurophysiol.107, 1998, 387–394.

De Clercq W., Vergult A., Vanrumste B., Van Paesschen W., Van Huffel S.: Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng 2006, 53:2583–7.

Delorme A., Sejnowski T., Makeig S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis, NeuroImage 34, 2007, 1443–1449.

Fatourechi M., Bashashati A., Ward RK., Birch GE.: EMG and EOG artifacts in brain computer interface systems: a survey. Clin Neurophysiol, 2007; 118:480–94.

Frank R.M., Frishkoff G.A.: Automated protocol for evaluation of electromagnetic component separation (APECS): application of a framework for evaluating statistical methods of blink extraction from multichannel EEG, Clin. Neurophysiol. 118, 2007, 80–97.

Goncharova II., McFarland DJ., Vaughan TM., Wolpaw JR.: EMG contamination of EEG: spectral and topographical characteristics. Clin Neurophysiol 2003; 114:1580–93.

Greco A., Mammone N., Morabito F., Versaci M.: Kurtosis, Renyi’s entropy and independent component scalp maps for the automatic artifact rejection from EEG data, International Journal of Signal Processing 2 (4), 2006, 240–244.

James C., Gibson O.: Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis, IEEE Transactions on Biomedical Engineering 50 (9), 2003, 1108–1116.

Jervis B.W., Coelho M., Morgan G.W.: Effect on EEG responses of removing ocular artefacts byproportional EOG subtraction, Med. Biol. Eng. Comput 27, 1989, 484–490.

Joyce C.A., Gorodnitsky I.F., Kutas M.: Automatic removal of eye movement and blink artifacts from EEG data using blind components eparation, Psychophysiology 41, 2004, 313–325.

Jung TP., Humphries C., Lee T, Makeig S., McKeown M.J., Iragui V., Sejnowski T.J.: Extended ICA removes artifacts from electroencephalographic recordings, Adv. NeuralInform. Process. Syst. 10, 1998, 894–900.

Jung TP., Makeig S., Humphries C., Lee TW., McKeown MJ., Iragui V., et al.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 2000, 37:163–78.

Kierkels J., van Boxtel G., Vogten L.: A model-based objective evaluation of eye movement correction in EEG recordings, IEEE Transactions on Biomedical Engineering 53 (2), 2006, 246–253.

Klemm M., Haueisen J., Ivanova G.: Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity, Medical & Biological Engineering & Computing 47, 2009, 413–423.

Lei X., Yang P., Yao D.: An empirical Bayesian framework for brain–computer interfaces, IEEETrans.NeuralSyst.Rehabil.Eng.17, 2009, 521–529.

LeVan P., Urrestarazu E., Gotman J.: A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification, Clinical Neurophysiology 117 (4), 2006, 912–927.

Li Y., Ma Z., Lu W., Li Y.: Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach, Physiological Measurement, 27 (4), 2006, 425.

Liu T., Yao D.: Removal of the ocular artifacts from EEG data using a cascaded spatio-temporal processing,Comput.MethodsProgr.Biomed. 83, 2006, 95–103.

Ma J., Bayram S., Tao P., Svetnik V.: High-throughput ocular artifact reduction in multichannel electroencephalography (EEG) using component subspace projection. J. Neurosci. Meth. 2011, 196:131–40.

Ma J., Tao P.,, Bayram S., Svetnik, V.: Muscle artifacts in multichannel EEG: Characteristics and reduction. Clinical Neurophysiology 123, 2012, 1676–1686.

Makeig S., Bell AJ., Jung TP., Sejnowski TJ.: Independent component analysis of electroencephalographic data. In: Advances in neural information processing systems. Cambridge, Mass: MIT Press 1996, 8:145–51.

Melissant C., Ypma A., Frietman E., Stam C.: A method for detection of Alzheimer’s disease using ICA-enhanced EEG measurements, Artificial Intelligence in Medicine 33 (3), 2005, 209–222.

Nicolaou N., Nasuto S.: Automatic artefact removal from event-related potentials via clustering, Journal of VLSI Signal Processing 48 (1), 2007, 173–183.

Qin Y., Xu P., Yao D.: A comparative study of different references for EEG default mode network: the use of the infinity reference, Clin. Neurophysiol. 121, 2010, 1981–1991.

Romero S., Mananas M., Barbanoj M.: A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case, Computers in Biology and Medicine 38 (3), 2008, 348–360.

Shao S., Shen K., Ong C., Wilder-Smith E., Li X.: Automatic EEG artifact removal: a weighted support-vector-machine approach with error correction, IEEE Transactions on Biomedical Engineering 56 (2), 2009, 336–344.

Ting K., Fung P., Chang C., Chan F.: Automatic correction of artifact from singletrial event-related potentials by blind source separation using second order statistics only, Medical Engineering and Physics 28 (8), 2006, 780–794.

Urrestarazu E., Iriarte J., Alegre M., Valencia M., Viteri C., Artieda J.: Independent component analysis removing artifacts in ictal recordings. Epilepsia 2004, 45:1071–8.

Vázquez R., Vélez-Péreza, H., Rantab R., Dorr V., Maquin D., Maillard L.: Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling. Biomedical Signal Processing and Control 7, 2012, 389–400

Vigario R.N.: Extraction of ocular artefacts from EEG using independent component analysis, Electroencephalogr.Clin.Neurophysiol.103, 1997, 395–404.

Wallstrom G., Kass R., Miller A., Cohn J., Fox N.: Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods, International Journal of Psychophysiology 53 (2), 2004, 105–119.

Wang Z., Peng X., TieJun L., Yin T., Xu L., DeZhong Y.: Robust removal of ocular artifacts by combining Independent Component Analysis and system identification. Biomedical Signal Processing and Control,10, 2014, 250–259.

Żygierewicz J., Malinowska U., Suffczyński P., Piotrowski T., Durka P.: Event-related desynchronization and synchronization in evoked K-complexes. Acta Neurobiologiae Experimentalis, 69, 2009, 254-261.

Żygierewicz J., Mazurkiewicz J., Durka P., Franaszczuk P., Crone N.: Estimation of short-time cross-correlation between frequency bands of event related EEG. Journal Of Neuroscience Methods, 157, 2, 2006, 294-302.

Published : 2015-06-30

Plechawska-Wójcik , M. (2015). METHODS OF EEG ARTIFACTS ELIMINATION. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 5(2), 39-46.

Małgorzata Plechawska-Wójcik
Politechnika Lubelska, Instytut Informatyki  Poland