Skip to main content
SpringerLink
Log in

A deep wavelet sparse autoencoder method for online and automatic electrooculographical artifact removal

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Electrooculographical (EOG) artifacts are problematic to electroencephalographical (EEG) signal analysis and degrade performance of brain–computer interfaces. A novel, robust deep wavelet sparse autoencoder (DWSAE) method is presented and validated for fully automated EOG artifact removal. DWSAE takes advantage of wavelet transform and sparse autoencoder to become a universal EOG artifact corrector. After being trained without supervision, the sparse autoencoder performs EOG correction on time–frequency coefficients collected after brain wave signal wavelet decomposition. Corrected coefficients are then used for wavelet reconstruction of uncontaminated EEG signals. DWSAE is compared with five other methods: second-order blind identification, information maximization, joint approximation diagonalization of eigen-matrices, wavelet neural network (WNN) and wavelet thresholding (WT). Experimental results on a visual attention task dataset, a mental state recognition dataset and a semi-simulated contaminated EEG dataset show that DWSAE is capable of suppressing EOG artifacts effectively, while preserving the nature of background EEG signals. The mean square error of signals before and after correction by DWSAE on a semi-simulated contaminated EEG segment of 30 s is the lowest (65.62) when compared to the results produced by WNN and WT. DWSAE addresses limitations posed by these methods in three ways. First, DWSAE can be performed automatically and online in a single channel of EEG data; this has advantages over independent component analysis-based methods. Second, its results are robust and stable in comparison with those of other wavelet-based methods. Third, as an unsupervised learning scheme, DWSAE does not require the off-line training that is necessary for WNN and other supervised learning machine learning-based methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. McFarland DJ, Wolpaw JR (2017) EEG-based brain–computer interfaces. Curr Opin Biomed Eng 4:194–200

    Article  Google Scholar 

  2. Ramadan RA, Vasilakos AV (2017) Brain computer interface: control signals review. Neurocomputing 223:26–44

    Article  Google Scholar 

  3. Naseer N, Hong KS (2015) fNIRS-based brain-computer interfaces: a review. Front Human Neurosci 9:3

    Google Scholar 

  4. Zhang R, Li Y, Yan Y, Zhang H, Wu S, Yu T, Gu Z (2016) Control of a wheelchair in an indoor environment based on a brain–computer interface and automated navigation. IEEE Trans Neural Syst Rehabil Eng 24(1):128–139

    Article  Google Scholar 

  5. Lalor EC, Kelly SP, Finucane C, Burke R, Smith R, Reilly RB, Mcdarby G (2005) Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP J Appl Sig Process 1(2005):3156–3164

    MATH  Google Scholar 

  6. Townsend G, LaPallo BK, Boulay CB, Krusienski DJ, Frye GE, Hauser C, Schwartz NE, Vaughan TM, Wolpaw JR, Sellers EW (2010) A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 121(7):1109–1120

    Article  Google Scholar 

  7. Loo SK, Lenartowicz A, Makeig S (2016) Research review: use of EEG biomarkers in child psychiatry research–current state and future directions. J Child Psychol Psychiatry 57(1):4–17

    Article  Google Scholar 

  8. Vaid S, Singh P, Kaur C (2015) EEG signal analysis for BCI interface: a review. In: 2015 Fifth international conference on advanced computing & communication technologies (ACCT), pp 143–147

  9. Fries P (2015) Rhythms for cognition: communication through coherence. Neuron 88(1):220–235

    Article  Google Scholar 

  10. Urigüen JA, Garcia-Zapirain B (2015) EEG artifact removal—state-of-the-art and guidelines. J Neural Eng 12(3):031001

    Article  Google Scholar 

  11. Maddirala AK, Shaik RA (2016) Removal of EOG artifacts from single channel EEG signals using combined singular spectrum analysis and adaptive noise canceler. IEEE Sens J 16(23):8279–8287

    Google Scholar 

  12. Hagemann D, Naumann E (2001) The effects of ocular artifacts on (lateralized) broadband power in the EEG. Clin Neurophysiol 112(2):215–231

    Article  Google Scholar 

  13. Krishnaveni V, Jayaraman S, Aravind S, Hariharasudhan V, Ramadoss K (2006) Automatic identification and removal of ocular artifacts from EEG using wavelet transform. Meas Sci Rev 6(4):45–57

    Google Scholar 

  14. Li X, Guan C, Zhang H, Ang KK (2017) Discriminative ocular artifact correction for feature learning in EEG analysis. IEEE Trans Biomed Eng 64(8):1906–1913

    Article  Google Scholar 

  15. Yang B, Duan K, Fan C, Hu C, Wang J (2018) Automatic ocular artifacts removal in EEG using deep learning. Biomed Signal Process Control 43:148–158

    Article  Google Scholar 

  16. Fatourechi M, Bashashati A, Ward RK, Birch GE (2007) EMG and EOG artifacts in brain computer interface systems: a survey. Clin Neurophysiol 118(3):480–494

    Article  Google Scholar 

  17. Barry RJ, Clarke AR, Johnstone SJ, Magee CA, Rushby JA (2007) EEG differences between eyes-closed and eyes-open resting conditions. Clin Neurophysiol 118:2765–2773

    Article  Google Scholar 

  18. Islam MK, Rastegarnia A, Yang Z (2016) Methods for artifact detection and removal from scalp EEG: a review. Neurophysiol Clin Clin Neurophysiol 46(4–5):287–305

    Article  Google Scholar 

  19. Jung TP, Makeig S, Humphries C, Lee TW, Mckeown MJ, Iragui V, Sejnowski TJ (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37(2):163–178

    Article  Google Scholar 

  20. Krishnaveni V, Jayaraman S, Anitha L, Ramadoss K (2006) Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J Neural Eng 3(4):338

    Article  Google Scholar 

  21. Nguyen HAT, Musson J, Li F, Wang W, Zhang G, Xu R, Richey C, Schnell T, McKenzie FD, Li J (2012) EOG artifact removal using a wavelet neural network. Neurocomputing 97:374–389

    Article  Google Scholar 

  22. Minguillon J, Lopez-Gordo MA, Pelayo F (2017) Trends in EEG-BCI for daily-life: requirements for artifact removal. Biomed Signal Process Control 31(31):407–418

    Article  Google Scholar 

  23. Dursun M, Özşen S, Yücelbaş C, Yücelbaş Ş, Tezel G, Küççüktürk S, Yosunkaya Ş (2017) A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification. Neural Comput Appl 28(10):3095–3112

    Article  Google Scholar 

  24. Gao J, Lin P, Yang Y, Wang P, Zheng C (2010) Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning. Neural Comput Appl 19(8):1217–1226

    Article  Google Scholar 

  25. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layerwise training of deep networks. In: Proc. NIPS, Vancouver

  26. Goodfellow I, Le Q, Saxe A, Lee H, Ng A (2009) Measuring invariances in deep networks. In: Proc. NIPS, Vancouver, pp 646–654

  27. Turnip A (2015) Comparison of ICA-based JADE and SOBI methods EOG artifacts removal. J Med Bioeng 4(6):436–440

    Google Scholar 

  28. Belouchrani A, Abed-Meraim K, Cardoso JF, Moulines E (1997) A blind source separation technique using second-order statistics. IEEE Trans Signal Process 45(2):434–444

    Article  Google Scholar 

  29. Tang AC, Sutherland MT, McKinney CJ (2005) Validation of SOBI components from high-density EEG. NeuroImage 25(2):539–553

    Article  Google Scholar 

  30. Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159

    Article  Google Scholar 

  31. He T, Clifford G, Tarassenko L (2006) Application of independent component analysis in removing artefacts from the electrocardiogram. Neural Comput Appl 15(2):105–116

    Article  Google Scholar 

  32. Zeng N, Zhang H, Song B, Liu W, Li Y, Dobaie AM (2018) Facial expression recognition via learning deep sparse autoencoders. Neurocomputing 273:643–649

    Article  Google Scholar 

  33. Wang YB, You ZH, Li X, Jiang TH, Chen X, Zhou X, Wang L (2017) Predicting protein–protein interactions from protein sequences by a stacked sparse autoencoder deep neural network. Mol BioSyst 13(7):1336–1344

    Article  Google Scholar 

  34. Cho K (2013) Simple sparsification improves sparse denoising autoencoders in denoising highly corrupted images. In: International conference on machine learning, pp 432–440

  35. Yang J, Bai Y, Li G, Liu M, Liu X (2015) A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression. Bio-Med Mater Eng 26(s1):S1549–S1558

    Article  Google Scholar 

  36. Qiu Y, Zhou W, Yu N, Du P (2018) Denoising sparse autoencoder-based ictal eeg classification. IEEE Trans Neural Syst Rehabil Eng 26(9):1717–1726

    Google Scholar 

  37. Zhang L, Ma W, Zhang D (2016) Stacked sparse autoencoder in PolSAR data classification using local spatial information. IEEE Geosci Remote Sens Lett 13(9):1359–1363

    Article  Google Scholar 

  38. Zhu C, Byrd RH, Lu P, Nocedal J (1997) Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans Math Softw (TOMS) 23(4):550–560

    Article  MathSciNet  Google Scholar 

  39. Daubechies I (1992) Ten lectures on wavelets, vol 61. Siam, Philadelphia

    Book  Google Scholar 

  40. Yu C, Manry MT, Li J, Narasimha PL (2006) An efficient hidden layer training method for the multilayer perceptron. Neurocomputing 70(1–3):525–535

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by project CS’20.03, Institute of Information Technology, Vietnam Academy of Science and Technology and project KC-4.0-07/19-25, Program KC4.0/19-25, Ministry of Science and Technology, Vietnam.

Author information

Authors and Affiliations

  1. Institute of Information Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Hanoi, Vietnam

    Hoang-Anh The Nguyen

  2. University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam

    Thanh Ha Le & The Duy Bui

Authors
  1. Hoang-Anh The Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thanh Ha Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. The Duy Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hoang-Anh The Nguyen.

Ethics declarations

Conflict of interest

The authors, whose names are listed immediately below, Hoang-Anh The Nguyen, Thanh Ha Le and The Duy Bui certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; or expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, HA.T., Le, T.H. & Bui, T.D. A deep wavelet sparse autoencoder method for online and automatic electrooculographical artifact removal. Neural Comput & Applic 32, 18255–18270 (2020). https://doi.org/10.1007/s00521-020-04953-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04953-0

Keywords

Search

Navigation