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Inventory reference ISSN 1812-7231 Klin.inform.telemed. Volume 14, Issue 15, 2019, Pages 13-34

Author(s) I. V. Redka


Kharkiv Medical Academy of Postgraduate Education, Ukraine

Kharkiv National University named after V. N. Karazin, Ukraine

Article title Modern approaches to detecting and removing artifacts from EEG signals. Overview

Abstract (resume)

Introduction. The detection of artifacts and their subsequent removal from EEG data is an important problem in neurophysiology, since the characteristics of artifact fragments overlap with those of true brain activity.

The aim is a review of existing mathematical approaches to the recognition and removal of artifacts from EEG signals.

Results. There are the methods based on statistical artifact rejection, adaptive filtration, regression, blind source separation, empirical mode decomposition, wavelet transform and their combination. Each of them has its advantages and disadvantages. Nevertheless, there is no universal algorithm for all types of artifacts. Choosing an artifact removal algorithm is mainly based on a presence of a reference channel, a method for detecting artifacts (automated or expert), and an artifact detection/removal mode (оn-line or off-line). It is quite difficult to compare different methods of artifact rejection due to the use of different metrics based on their ability to remove artifacts and the degree of distortion of the output signal.

Conclusion. Independent Components Analysis is the most commonly used in neurophysiological research. The priority is the development of hybrid methods for the removal of physiological artifacts. A three-step verification of the effectiveness of the new algorithm for identifying/removing artifacts is advisable.

Keywords EEG, Artifacts, Regression, Adaptive filtering, Blind separation of sources, Empirical mode decomposition, Wavelet-transform, Threshold values


1. Hammond D. C., Gunkelman J. The art of artifacting. USA, Salt Lake City, Utah, Society for Neuronal Regulation, 2001, 111 p.

2. Nunez P. L., Srinivasan R. Electric Fields of the Brain. UK, Oxford Univ. Press, 2006, 611 р.

3. Klados M. A., Papadelis C., Bamidis P. D., Braun C. REG-ICA: A hybrid methodology combining blind source separation and regression techniques for the rejection of ocular artifacts. Biomed. Signal Process. Control., 2011, vol. 6, no. 3, pp. 291–300.

4. Nicolas-Alonso L. F., Gomez-Gil J. Brain computer interfaces, a review. Sensors, 2012, vol. 12, no. 2, pp. 1211–1279.
PMid:22438708 PMCid:PMC3304110

5. Ochoa C. J., Polich J. P300 and blink instructions. Clin. Neurophysiol., 2000, vol. 111, no. 1, pp. 93–98.

6. Vergult A., De Clercq W., Palmini A., Vanrumste B., Dupont P., Van Huffel S. and Van Paesschen W. Improving the Interpretation of Ictal Scalp EEG: BSS–CCA Algorithm for Muscle Artifact Removal. Epilepsia, 2007, vol. 48, no. 5, pp. 950–958.

7. Keil A., Debener S., Gratton G., Junghöfer M., Kappenman E. S., Luck S. J., Luu P., Miller G. A., Yee C. M. Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. Psychophysiol., 2014, vol. 51, no. 1, pp. 1–21.

8. Gulyaev S. A., Arkhipenko I. V. Artifacts in an electroencephalographic study: identification and differential diagnosis. Russkij zhurnal detskoj nevrologi [Russ. J. of Pediatric Neurologists]. 2012, vol. VII, no. 3, pp. 3–16. (In Russ.).

9. Jiang X., Bian G. B., Tian Z. Removal of Artifacts from EEG Signals: A Review. Sensors (Basel), 2019, vol. 19, no. 5, article 987, 18 p.
PMid:30813520 PMCid:PMC6427454

10. Stone D. B., Tamburro G., Fiedler P., Haueisen J., Comani S. Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications. Frontiers in human neuroscience, 2018, vol. 12, article 96, 15 p.
PMid:29618975 PMCid:PMC5871683

11. Islam K., Rastegarnia A., Yang Zhi., Methods for artifact detection and removal from scalp EEG: A review, Neurophysiol. Clin./Clin. Neurophysiol., 2016, vol. 46, iss. 4–5, pp. 287–305.

12. Tereshchenko E. P., Ponomarev V. A., Kropotov Yu. D., Müller A. Comparative efficiencies of different methods for removing blink artifacts in analyzing quantitativ electroencephalogram and event-related potentials. Human Physiology, 2009, vol. 35, no. 2, pp. 241–247.

13. Romero S., Maсanas M. A., Barbanoj M. J. Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation. Ann. Biomed. Eng., 2009, vol. 37, no. 1, pp. 176–191.

14. Shoker L., Sanei S., Chambers J. Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Process. Letters, 2005, vol. 12, no. 10, pp. 721–724.

15. Wallstrom G. L., Kass R. E., Miller A., Cohn J. F., Fox N. A. Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. Int. J. Psychophysiol., 2004, vol. 53, no. 2, pp. 105–119.

16. Nolan H., Whelan R. Reilly R. B. FASTER: fully automated statistical thresholding for EEG artifact rejection. J. Neurosci. Methods, 2010, vol. 192, pp. 152–162.

17. Mognon A., Jovicich J., Bruzzone L., Buiatti M. ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiol., 2011, vol. 48, no. 2, pp. 229–240.

18. Merinov P. A., Belyaev M. G. The influence of automatic methods for cleaning EEG from artifacts on the accuracy of classification. Cbornik trudov 39-j mezhdiscip. shkoly-konf. NPPI RAN "Informacionnye tehnologii i sistemy 2015". [Proc. of the 39th Interdiscipline. Conf. NPPI RAS "Information Technologies and Systems 2015"], 2015, pp. 313–328. (In Russ.).

19. Tuyisenge V., Trebaul L., Bhattacharjee M., Chanteloup-Forêt B., Saubat-Guigui C., Mоndruţă I., Rheims S., Maillard L., Kahane P., Taussig D., David O. Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning. Clin Neurophysiol., 2018, vol. 129, no. 3, pp. 548–554.

20. Gabard-Durnam L. J., Mendez Leal A. S., Wilkinson C. L., Levin A. R. The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data. Front Neurosci., 2018, vol. 12, article 97, 24 p.
PMid:29535597 PMCid:PMC5835235

21. Tamburro G., Stone D. B., Comani S. Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data. Front Neurosci., 2019, vol. 13, article 441, 17 p.

22. Dammers J., Schiek M., Boers F., Silex C., Zvyagintsev M., Pietrzyk U., Mathiak K. Integration of Amplitude and Phase Statistics for Complete Artifact Removal in Independent Components of Neuromagnetic Recordings. IEEE Trans actions on Biomed. Eng., 2008б vol. 55, no. 10, pp. 2353–2362.

23. Delorme A., Sejnowski T., Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage, 2006, vol. 34, no. 4, pp. 1443–1449.

24. Greco A., Mammone N., Morabito F. C., Versaci M. Kurtosis, Renyi’s entropy and independent component scalp maps for the automatic artifact rejection from EEG data. Signal Processing. Intern. J. of Biomed. and Biol. Eng., 2008, vol. 2, no. 9, pp. 344–348.

25. Escudero J., Hornero R., Abásolo D. Fernández A. Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation. Ann. Biomed. Eng., 2011, vol. 39, no. 8, pp. 2274–2286.

26. Urigüen J. A., Garcia-Zapirain B., Artieda J., Iriarte J., Valencia M. Comparison of background EEG activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster-based permutation statistical testing. PLoS ONE, 2017,, vol. 12, no. 9, article e0184044, 15 p.

27. Wu S.-D., Wu C.-W., Lin S.-G., Wang C.-C., Lee K.-Y. Time series analysis using composite multiscale entropy. Entropy, 2013, vol. 15, no. 3, pp. 1069–1084.

28. Bosl W., Tierney A., Tager-Flusberg H., Nelson H. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med., 2011, vol. 9, article 18, 16 p.
PMid:21342500 PMCid:PMC3050760

29. Mannan M. M. N., Kamran M. A., Jeong M. Y. Identification and Removal of Physiological Artifacts from Electroencephalogram Signals: A Review. IEEE Access, 2018. vol. 6, pp. 30630–30652

30. Picton T. W., Van Roon P., Armilio M. L., Berg P., Ille N., Scherg M. The correction of ocular artifacts: a topographic perspective. Clin. Neurophysiol., 2000, vol. 111, no. 1, pp. 53–65.

31. Liu W., Park I., Wang Y., Principe J. C. Extended kernel recursive least squares algorithm. IEEE Trans. Signal. Process., 2009, vol. 57, no. 10, pp. 3801–3814.

32. Mannan M. M., Jeong M. Y., Kamran M. A. Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals. Frontiers in human neurosci., 2016, vol. 10, article 193, 17 p.

33. Poller B. V., Schetinin Yu. I., Orlov I. S. Adaptive digital signal filtering in electroencephalogram analysis systems. Nauchnyj vestnik NGTU [Scientific Bulletin of NSTU]. 2013, vol. 50, no. 1, pp. 31–38. (In Russ.).

34. Correa A. G., Laciar E., Patiño H. D., Valentinuzzi M. E. Artifact removal from EEG signals using adaptive filters in cascade. J. Physics: Conference Series, 2007, vol. 90, article 012081, 10 p.

35. Rangayyan R. M. Analiz biomeditsinskikh signalov. Prakticheskiy podkhod. [Analysis of biomedical signals. The practical approach]. English transl. Ed. by A. P. Nemirko. Moskow, FIZMATLIT Publ., 2010. 440 p. (In Russ.).

36. Drobotko D. V., Shevchenko A. I., Drobotko V. F., Kachur I. V. Off-line emission detection and purification of intracranial pressure monitoring signals. Iskusstvennyj intellect [Artificial Intelligence], 2013, no. 3, pp. 495–506. (In Russ.).

37. Ferdous J., Ali M. S. A Comparison of Wiener and Kalman Filters for the Artifact Suppression from EEG Signal. Intern. J. of Sci. and Res., 2017, vol. 6, iss. 4, pp. 2029–2035.

38. Fitzgibbon S., Powers D., Pope K., Clark C. Removal of EEG Noise and Artifact Using Blind Source Separation. J. Clin. Neurophysiol., 2007, vol. 24. no. 3, pp. 232–243.

39. Berg P., Scherg M. Dipole models of eye activity and its application to the removal of eye artifacts from the EEG ad MEG. Clin. Physiol. Meas., 1991, vol. 12, no. Suppl. A, pp. 49–54.

40. Urigüen J. A., Garcia-Zapirain B. EEG artifact removal – stateof-the-art and guidelines. J. Neural Eng., 2015, vol. 12, no. 3, article 031001, 44 p.

41. Kitsun P. G. Using the third-party method to automatically remove EEG artifacts associated with eye movements. Visnik Nacional. Tehnich. Univer. Ukrayini "Kiyivskij politeh. institut". Seriya : Radiotehnika. Radioaparatobuduvannya [Bulletin of the Nat. Tech. Univer. of Ukraine "Kyiv Polytechnic Institute". Series: Radio Eng.]. 2016, vol. 65, pp. 99–107. (In Ukr.).

42. Abdullah A. A., Zhang C. Z., Abdullah A., Lian, S. Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm. Math. Probl. in Eng., 2014, vol. 2014, article ID 324750, 25 p.

43. Clercq W. D., Vergult A., Vanrumste B., Paesschen W. V., Huffel S. V. Canonical Correlation Analysis Applied to Remove Muscle Artifacts from the Electroencephalogram. IEEE Trans. on Biomed. Eng., 2006, vol. 53, no. 12 (Pt 1), pp. 2583–2587.

44. Gao J., Zheng C., Wang, P. Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis Clin. EEG Neurosci., 2010. vol. 41, no. 1, pp. 53–59.

45. Hara S., Kawahara Y., Washio T., von Bünau P., Tokunaga T. Separation of stationary and non-stationary sources with a generalized eigenvalue problem. Neural Netw., 2012, vol. 33, pp. 7–20.

46. Von Bünau P., Meinecke F. C., Király F. C., Mьller K. R. Finding stationary subspaces in multivariate time series. Phys. Rev. Lett., 2009, vol. 103, no. 21, pp. 214101–214105.

47. Safieddine D., Kachenoura A., Albera L., Birot G., Karfoul Ah., Pasnicu A., Biraben A., Wendling F., Senhadji L., Merlet I. Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches. EURASIP J. Adv. Signal Process., 2012, article 127.

48. Tsai F.-F., Fan S.-Z., Lin Y.-S., Huang N. E., Yeh J.-R. Investigating Power Density and the Degree of Nonlinearity in Intrinsic Components of Anesthesia EEG by the Hilbert-Huang Transform: An Example Using Ketamine and Alfentanil. PLoS ONE, 2016, vol. 11, no. 12, article e0168108, 16 p.
PMid:27973590 PMCid:PMC5156388

49. Huang N. E., Shen Z., Long S. R., Wu M. C., Shih H. H., Zheng Q., Yen N.-C., Tung C. C., Liu H. H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. A Math. Phys. Eng. Sci., 1998, vol. 454, iss. 1971, pp. 903–995.

50. Rashed-Al-Mahfuz M., Islam M. R., Hirose K., Molla M. K. Artifact suppression and analysis of brain activities with electroencephalography signals. Neural Regen Res. 2013, vol. 8, no. 16, pp. 1500–1513.

51. Roy V., Shukla S., Shukla P. K., Rawat P. Gaussian Elimination-Based Novel Canonical Correlation Analysis Method for EEG Motion Artifact Removal. J Healthc Eng., 2017, article 9674712, 11 p.

52. Peng H., Hu B., Shi Q., Ratcliffe M., Zhao Q., Qi Y., Gao G. Removal of ocular artifacts in EEG–an improved approach combining DWT and ANC for portable applications. IEEE J. Biomed. Health Inform., 2013, vol. 17, no. 3, pp. 600–607.

53. Bono V., Das S., Jamal W., Maharatna K. Hybrid wavelet and EMD/ICA approach for artifact suppression in pervasive EEG. J. Neurosci. Methods, 2016, vol. 267, pp. 89–107.

54. Chen X., Liu A., Chiang J., Wang Z. J., McKeown M. J., Ward R. K. Removing muscle artifacts from EEG data: Multichannel or single-channel techniques? IEEE Sensors J., 2016, vol. 16, no. 7, pp. 1986–1997.

55. Ghandeharion H., Erfanian A. A fully automatic ocular artifact suppression from EEG data using higher order statistics: improved performance by wavelet analysis. Med. Eng. Phys., 2010, vol. 32, no. 7, pp. 720–729.

56. Sweeney K. T., Ward T. E., McLoone S. F. Artifact removal in physiological signals — practices and possibilities. IEEE Trans. Inf. Technol. Biomed., 2012, vol. 16, no. 3, pp. 488–500.

57. Croft R. J., Barry R. J. Removal of ocular artifacts from the EEG: a review. J. Clin. Neurophysiol., 2000, vol. 30, no. 1, pp. 5–19.

58. Waser M., Garn H. Removing cardiac interference from the electroencephalogram using a modified Pan-Tompkins algorithm and linear regression. In Proc. of the 35th Ann. Intern. Conf. of the IEEE Eng. in Medicine and Biol. Society (EMBC’13), 2013, pp. 2028–2031.

59. Kanoga S., Kanemura A., Asoh H. Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms. Neurocomputing, 2019, vol. 347, pp. 240–250.

60. Jung T.-P., Humphries C., Lee T.-W., Makeig S., McKeown M. J., Iragui V., Sejnowski T. J. Removing electroencephalographic artifacts: comparison between ICA and PCA. Neural Networks for Signal Process. VIII. Proc. of the 1998 IEEE Signal Pro cess. Society Workshop (Cat. No.98TH8378), Cambridge, 1998, pp. 63–72.

61. Mneimneh M., Yaz E. E., Johnson M. T., & Povinelli R. J. An adaptive Kalman filter for removing baseline wandering in ECG signals. Computers in Cardiol., 2006, vol. 33, pp. 253–256.

62. Morbidi F., Garulli A., Prattichizzo D., Rizzo C., Rossi S. Application of Kalman filter to remove TMS-induced artifacts from EEG recordings. IEEE Transactions on control system technology, 2008, vol. 16, no. 6, pp. 1360–1366.

63. Somers B., Francart T., Bertrand A. A generic EEG artifact removal algorithm based on the multichannel Wiener filter. J. of Neural Eng., 2018, vol. 15, no. 3, article 036007, 12 p.

64. Nandi Sh., Ferdous J. Comparison between DWT and SWT Algorithms for the Suppression of Muscular Artifact from ictal EEG. Intern. J. of Comput. Sci. and Inform. Security, 2019, vol. 17, no. 6, pp. 1–7.

65. Navarro X., Porée F., Beuchée A., Carrault G. Denoising preterm EEG by signal decomposition and adaptive filtering: A comparative study. Med. Eng. Phys., 2015. vol. 37, no. 3, pp. 315–320.

66. Mahajan R., Morshed B. I. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy kurtosis and wavelet-ICA. IEEE J. Biomed. Health Informat., 2015, vol. 19, no. 1, pp. 158–165.

67. Raghavendra B., Dutt D. N. Wavelet enhanced CCA for minimization of ocular and muscle artifacts in EEG. World Acad. Sci. Eng. Technol., 2011, vol. 57, no. 6, pp. 1027–1032.

68. Al-Qazzaz N. K., Ali S. H. B. M., Ahmad S. A., Islam M. S., Escudero J. Automatic artifact removal in EEG of normal and demented individuals using ICA-WT during working memory tasks. Sensors, 2017, vol. 17, no. 6, E1326. – 25 p.
PMid:28594352 PMCid:PMC5492863

69. Chen X., Liu A., Chen Q., Liu Y., Zou L., Mckeown M. J. Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics. Comput. Biol. Med., 2017, vol. 88, iss. C, pp. 1–10.

70. Lindsen J. P., Bhattacharya J. Correction of blink artifacts using independent component analysis and empirical mode decomposition. Psychophysiol., 2010. vol. 47, no. 5, pp. 955–960.

71. Wang G., Teng C., Li K., Zhang Z., Yan X. The removal of EOG artifacts from EEG signals using independent component analysis and multivariate empirical mode decomposition. IEEE J. Biomed. Health Informat., 2016. vol. 20, no. 5, pp. 1301–1308.

72. Mowla M. R., Ng S. C., Zilany M. S. A., Paramesran R. Artifactsmatched blind source separation and wavelet transform for multichannel EEG denoising. Biomed. Signal Process. and Control, 2015, vol. 22, pp. 111–118.

73. Mingai L., Shuoda G., Guoyu Z., Yanjun S., Jinfu Y., Removing ocular artifacts from mixed EEG signals with FastKICA and DWT. J. Intell. Fuzzy Syst., 2015, vol. 28, no. 6, pp. 2851–2861.

74. Zeng K., Chen D., Ouyang G., Wang L., Li X., Li X., An EEMD-ICA approach to enhancing artifact rejection for noisy multivariate neural data. IEEE Trans. Neural Syst. Rehabil. Eng., 2016, vol. 24, no. 6, pp. 630–638.

75. Patel R., Sengottuvel S., Janawadkar M. P., Gireesan K., Radhakrishnan T., Mariyappa N., Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis. Comput. Elect. Eng., 2016, vol. 54, pp. 78–86.

76. Chen X., He C., Peng H. Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis. J. of Appl. Math., 2014, vol. 2014, no. 261347, pp.1–10.

77. Chen X., Liu A., Peng H., Ward R. K. A preliminary study of muscular artifact cancellation in single-channel EEG. Sensors, 2014. vol. 14, no. 10, pp. 18370–18389.
PMid:25275348 PMCid:PMC4239950

78. Shahabi H., Moghimi S., & Zamiri-Jafarian H. EEG eye blink artifact removal by EOG modeling and Kalman filter. 5th Intern. Conf. on BioMed.Eng. and Informat., 2012, pp. 496–500.

79. Castellanos N. P., Makarov V. A. Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J. Neurosci. Methods, 2006, vol. 158 (2), pp. 300–312.

80. Islam M. K., Rastegarnia A., Yang Z. A Wavelet-Based Artifact Reduction from Scalp EEG for Epileptic Seizure Detection. Biomed. and Health Informat., 2015, vol. 99, pp.1–12.

81. Sweeney K. T., McLoone S. F., Ward T. E. The Use of Ensemble Empirical Mode Decomposition with Canonical Correlation Analysis as a Novel Artifact Removal Technique. Biomed. Eng., 2013, vol. 60, no. 1, pp. 97–105.

82. Sahul J. B. Z., Widrow B., Guilleminault C. EKG artifact cancellation from sleep EEG using adaptive filtering, Sleep Res. A, 1995, vol. 24, pp. 486.

83. Devuyst S., Dutoit T., Stenuit P., Kerkhofs M., Stanus E. Removal of ECG artifacts from EEG using a modified independent component analysis approach. EURASIP J. Adv. Signal Process., 2008, vol. 2008, no. 1, pp. 5204–5207.


84. Tong S., Bezerianos A., Paul J., Zhu Y., Thakor N. Removal of ECG interference from the EEG recordings in small animals using independent component analysis. J. Neurosci. Methods, 2001, vol. 108, no. 1, pp. 11–17.

85. Hamaneh M. B., Chitravas N., Kaiboriboon K., Lhatoo S. D., Loparo K. A. Automated removal of EKG artifact from EEG data using independent component analysis and continuous wavelet transformation. IEEE Trans. Biomed. Eng., 2014, vol. 61, no. 6, pp. 1634–1641.


86. Khorasani A., Shalchyan V., Daliri M. R. Adaptive Artifact Removal From Intracortical Channels for Accurate Decoding of a Force Signal in Freely Moving Rats. Front Neurosci. 2019, vol. 13, article 350, 12 p.

PMid:31040764 PMCid:PMC6476983

87. Akhtar M. T., Jung T.-P., Makeig S., Cauwenberghs G. Recur sive independent component analysis for online blind source separation. IEEE Circuits and Systems (ISCAS), 2012, pp. 2813–2816.

88. Huang K. J., Liao J. C., Shih W. Y., Feng C. W., Chang J. C., Chou C. C., Fang W. C. A Realtime Processing flow for ICA based EEG Acquisition system with Eyeblink Artifact Elimination. Signal Process. Systems (SiPS), 2013, pp. 237–240.

89. Daly I., Scherer R., Billinger M., Müller-Putz G. FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing. IEEE Transactions on Neural Syst. and Rehabilit. Eng., 2015, vol. 23, no. 5, pp. 725–736.

90. Khatun S., Mahajan R., Morshed B. I. Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG. In 2015 IEEE Intern. Conf. on Electro/Inform. Technol., EIT 2015, pp. 335–340.

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