Skip to main content
SpringerLink
Log in

An EEG Atomized Artefact Removal Algorithm: A Review

  • Conference paper
  • First Online:
Micro-Electronics and Telecommunication Engineering (ICMETE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 373))

  • 874 Accesses

Abstract

Signals from electroencephalograms (EEGs) are part of a growing body of biomedical data, which is used as EEG signals can be interfered with by artefacts created during the range and dispensation of the information, which can obscure the main features and information quality of signal. In order to confirm that the EEG signal does not lose any of its major attributes when diagnosing human neurological diseases such as epilepsy, tumours, and traumatic issues, these artefacts should be a review of EEG applications in health care is also provided, as well as a summary of challenges, research gaps, and future directions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mahadevan AA, Mugler D (2008) Ballistocardiogram artifact removal in EEG-fMRI signals using discrete Hermite transforms. Signal Process 2(2):839–853

    Google Scholar 

  2. Delorme A, Sejnowski T, Makeig S (2007) Enhanced detection of artefacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34(4):1443–1449

    Article  Google Scholar 

  3. Casson AJ, Villegas ER (2008) On data reduction in EEG monitoring: comparison between ambulatory and non-ambulatory recordings. International IEEE EMBS conference vancouver, pp 5885–5888

    Google Scholar 

  4. Mert A, Akan A (2013) Hilbert-Huang transform based hierarchical clustering for EEG denoising. Signal processing conference (EUSIPCO), vol 1–5

    Google Scholar 

  5. Qazzaz A, Ali NK, Ahmed S, Islam MS, Ariff MI (2015) Selection of mother wavelets thresholding method in denoising multichannel EEG signals during working memory task. Sensors 15:29015–29035

    Article  Google Scholar 

  6. Hassan AR, Bhuiyan MIH (2016) Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybernetics Biomed Eng 36:248–255

    Article  Google Scholar 

  7. Turnip A (2014) “JADE-ICA algorithm for EOG artifact removal in EEG recording. technology, informatics, management, engineering, and environment. TIME-E, pp 270–274

    Google Scholar 

  8. Mijovic BV, Huffel SV (2010) Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. Biomed Eng 57(9):2188–2196

    Google Scholar 

  9. Majmudar CA, Morshed BI (2015) Real-time hybrid ocular artifact detection and removal for single channel EEG. Electro/information technology (EIT). pp 330–334

    Google Scholar 

  10. Burger C, Heever D (2015) Removal of EOG artefacts by combining wavelet neural network and independent component analysis. Biomed Signal Process Control 15:67–79

    Article  Google Scholar 

  11. Mosquera CG, Vázquez AN (2010) Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data. Signal Process IET 6(2):99–106

    Article  MathSciNet  Google Scholar 

  12. Kuo CY, Wei SK, Tsai PW (2013) Ensemble empirical mode decomposition with supervised cluster analysis. Adv Adapt Data Anal 5(1):1–13

    Article  MathSciNet  Google Scholar 

  13. Teng CZ, Wang G (2014) The removal of EMG artifact from EEG signals by the multivariate empirical mode decomposition. Signal Processing, Communications and Computing, pp 873–876

    Google Scholar 

  14. Zhao C, Qiu T (2015) An automatic ocular artifacts removal method based on wavelet-enhanced canonical correlation analysis. Engineering in medicine and biology society (EMBC), pp. 4191–4194

    Google Scholar 

  15. Donoho DL, Johnstone IM, Kerkyacharian G, Picardi D (1995) Wavelet shrinkage: asymptopia? J R Stat Soc B 57:301–369

    MathSciNet  MATH  Google Scholar 

  16. Moretti DV, Babiloni F, Carducci F, Cincotti F, Remondini E, Rossini PM, Salinari S, Babiloni C (2003) Computerized processing of EEG–EOG–EMG artefacts for multicentric studies in EEG oscillations and event-related potentials. Int J Psychophysiol 47:199–216

    Article  Google Scholar 

  17. Hosna G, Erfanian A (2010) A fully automatic ocular artefact suppression from EEG data using higher order statistics: improved performance by wavelet analysis. Med Eng Phys 32(7):720–729

    Article  Google Scholar 

  18. Rilling G, Flandrin P, Calves PG, On empirical mode decomposition and its algorithms. IEEE-EURASIP workshop on nonlinear signal and image processing NSIP-03

    Google Scholar 

  19. Abdullah H, Cvetkovic D (2013) Double density wavelet for EEG signal denoising. International conference on machine learning and computer science (IMLCS'2013), Kuala Lumpur (Malaysia)

    Google Scholar 

  20. Ghandeharion H, Erfanian A (2006) A fully automatic method for ocular artifact suppression from EEG data using wavelet transform and independent component analysis. Engineering in medicine and biology societ. pp 5265–5268

    Google Scholar 

  21. Daly IS, Putz GM (2015) FORCe: fully online and automated artifact removal for brain-computer interfacing. Neural Syst Rehabil Eng 23(5):725–736

    Article  Google Scholar 

  22. Matiko JB, Tudor J (2013) Real time eye blink noise removal from EEG signals using morphological component analysis. Engineering in medicine and biology society (EMBC). pp 13–16

    Google Scholar 

  23. Hu JW, She J (2015) Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system. Neurocomputing 151:278–287

    Article  Google Scholar 

  24. Zheng J, Cheng J, Yang Y (2014) Partly EEMD: an improved noise assisted method for eliminating mode mixing. Signal Process 96:362–374

    Article  Google Scholar 

  25. Huang KJ, Liao JC, Shih WY, Feng CW, Chang JC, Chou CC, Fang WC (2013) A real-time processing flow for ICA based EEG acquisition system with eye blink artifact elimination. IEEE workshop on signal processing systems. pp 237–240

    Google Scholar 

  26. Huang KL, Fang W (2013) A realtime processing flow for ICA based EEG acquisition system with eyeblink artifact elimination. Signal processing systems (SiPS). pp 237–240

    Google Scholar 

  27. Sweeney KT, Onaral B (2012) A methodology for validating artifact removal techniques for physiological signals. Inf Technol Biomed 16(5):918–926

    Article  Google Scholar 

  28. Sweeney KT, Ward TE (2013) The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. Biomed Eng 60(1):97–105

    Google Scholar 

  29. Anastasiadou MC, Mitsis GD (2015) Automatic detection and removal of muscle artifacts from scalp EEG recordings in patients with epilepsy. Engineering in medicine and biology society. pp 1946–1950

    Google Scholar 

  30. Soomro MH, Jatoi MA (2013) Automatic eye-blink artifact removal method based on EMD-CCA. Complex medical engineering (CME). pp 186–190

    Google Scholar 

  31. Soomro MH, Yusoff M (2014) Comparison of blind source separation methods for removal of eye blink artifacts from EEG. Intelligent and advanced systems (ICIAS). pp 1–6

    Google Scholar 

  32. Anastasiadou MC, Mitsis GD (2014) Detection and removal of muscle artifacts from scalp EEG recordings in patients with epilepsy. Bioinformatics and bioengineering. pp 291–296

    Google Scholar 

  33. Betta MG, Menicucci D (2013) Detection and removal of ocular artifacts from EEG signals for an automated REM sleep analysis. Engineering in medicine and biology society (EMBC). pp 5079–5082

    Google Scholar 

  34. Jansen M, White TP, Mullinger KJ, Iddle EB, Gowland PA, Francis ST, Bowtell R, Liddle PF (2012) Motion-related artefacts in EEG predict neuronally plausible patterns of activation in fMRI data. Neuroimage 59(1):261–270

    Article  Google Scholar 

  35. Islam MK, Rastegarnia A, Yang Z (2015) A wavelet-based artifact reduction from scalp EEG for epileptic seizer detection. IEEE J Biomed Health Inform

    Google Scholar 

  36. Mowla MR, Paramesran R (2015) Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising. Biomed Signal Process Control 22:111–118

    Article  Google Scholar 

  37. Akhtar MT, James CJ (2012) Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. Signal Process 92:401–416

    Article  Google Scholar 

  38. Babu PA, Prasad K (2011) Removal of ocular artifacts from EEG signals using adaptive threshold PCA and wavelet transforms. Communication systems and network technologies. pp 572–576

    Google Scholar 

  39. Jadhav PA, Naik G (2014) Automated detection and correction of eye blink and muscular artefacts in EEG signal for analysis of autism spectrum disorder. Engineering in medicine and biology society. pp 1881–1884

    Google Scholar 

  40. Zhao QH, Peng H (2014) Automatic identification and removal of ocular artifact in EEG-imaproved adaptive predictor filtering for portable application. NanoBioscience 13(2):109–118

    Article  Google Scholar 

  41. Croft RJ, Barry RJ (2000) EOG correction: which regression should we use? Psychophysiology 37:123–125

    Article  Google Scholar 

  42. Mahajan R, Morshed BI (2014) Unsupervised eye blink artifact denoising of EEG data with modified multi-scale sample entropy, Kurtosis, and wavelet-ICA. Biomed Health Inf 19(1):158–165

    Google Scholar 

  43. Vazquez RR, Maquin D (2007) EEG ocular artefacts and noise removal. Engineering in medicine and biology society. pp 5445–5448

    Google Scholar 

  44. R. S. Patel and N. Mariyappa (2015) Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis. Comput Electr Eng

    Google Scholar 

  45. Haoni S, Chauxie W, Pandey M. Application of Hilbert-Huang transform in generating spectrum-compatible earthquake time histories. ISRN Signal Process 2011:1–17. Article Id 563678

    Google Scholar 

  46. Khatun SM, Morshed BI (2015) Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG. Electro/information technology (EIT), pp. 335–340

    Google Scholar 

  47. Postalcioglu S, Erkan K, Bolat ED (2005) Comparison of Kalman filter and wavelet filter for denoising. Int Conf Neural Networks Brain 2:951–954

    Google Scholar 

  48. Priyadharsini SS, Rajan SE (2014) An efficient method for the removal of ECG artefact from measured EEG signal using PSO algorithm. Int J Adv Soft Comput Appl 6(1):1–19

    Google Scholar 

  49. Sanei S, Chambers JA (2007) Introduction of EEG- EEG signal processing. Wiley. ISBN-10: 0470025816

    Google Scholar 

  50. Lagerlund TD, Sharbrough FW, Busacker NE (1997) Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. J Clin Neurophysiol 14(1):73–82

    Article  Google Scholar 

  51. Chiong TL (2006) Sleep: a comprehensive handbook. Wiley

    Google Scholar 

  52. Radüntz TS, Meffert B (2015) EEG artifact elimination by extraction of ICA-component features using image processing algorithms. J Neurosci Methods 243:84–93

    Article  Google Scholar 

  53. Emir U, Akgul CB, Akin A, Harmanci K (2003) Wavelet denoising versus ICA denoising for functional optical imaging. International IEEE EMBS conference on neural engineering. IEEE

    Google Scholar 

  54. Chang WC, Im C (2015) Detection of eye blink artifacts from single prefrontal channel electroencephalogram. Comput Methods Programs Biomed 124:19–30

    Article  Google Scholar 

  55. Hsu WL, Chen I (2012) Wavelet-based envelope features with automatic EOG artifact removal: application to single-trial EEG data. Expert Syst Appl 39:2743–2749

    Article  Google Scholar 

  56. Blume WT, Kaibara M, Young GB (2002) Atlas of adult electroencephalography. Lippincott Williams and Wilkins, Philadelphia

    Google Scholar 

  57. Chen XH, Peng H (2014) Removal of muscle artifacts from single-channel EEG based on ensemble empirical mode decomposition and multi-set canonical correlation analysis. J Appl Math 1–11

    Google Scholar 

  58. Chen XL, Ward RK (2014) A preliminary study of muscular artifact cancellation in single-channel EEG. Sensors 14:18370–18389

    Article  Google Scholar 

  59. Lu Y, Oruklu E, Saniie J (2013) Chirplet signal and empirical mode decompositions of ultrasonic signals for echo detection and estimation. J Signal Inf Process 4:149–157

    Google Scholar 

  60. Liu Z, Zhang Z (2015) The improved algorithm of the EMD decomposition based on cubic spline interpolation. Signal Process Res 4:63–68

    Article  Google Scholar 

  61. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(1):1–41

    Article  Google Scholar 

  62. Ge SH, Hong X (2014) A fully automatic ocular artifact removal from EEG based on fourth-order tensor method. Biomed Eng Letter 455–463

    Google Scholar 

  63. Sardouie SH, Merlet I (2015) An efficient Jacobi-like deflationary ICA algorithm: application to EEG denoising. Signal Process Lett 22(8):1198–1202

    Article  Google Scholar 

  64. Bizopoulos PA, Fotiadis DI (2013) An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition. Engineering in medicine and biology society. pp 5853–5856

    Google Scholar 

  65. Kanoga S, Mitsukura Y (2015) Eye blink artifact rejection in single-channel electroencephalographic signals by complete ensemble empirical mode decomposition and independent component analysis. Engineering in medicine and biology society. pp 121–124

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, St. Peters Institute of Higher Education and Research, Chennai, India

    Rudra Bhanu Satpathy & G. P. Ramesh

Authors
  1. Rudra Bhanu Satpathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. P. Ramesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ghaziabad, India

    Devendra Kumar Sharma

  2. National Taipei University of Business, Taipei, Taiwan

    Sheng-Lung Peng

  3. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ghaziabad, India

    Rohit Sharma

  4. Odessa State Environmental University, Odessa, Ukraine

    Dmitry A. Zaitsev

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Satpathy, R.B., Ramesh, G.P. (2022). An EEG Atomized Artefact Removal Algorithm: A Review. In: Sharma, D.K., Peng, SL., Sharma, R., Zaitsev, D.A. (eds) Micro-Electronics and Telecommunication Engineering . ICMETE 2021. Lecture Notes in Networks and Systems, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-16-8721-1_72

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-8721-1_72

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8720-4

  • Online ISBN: 978-981-16-8721-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation