Skip to main content
Springer Nature Link
Log in

Automatic Removal of Artifacts from Attention Deficit Hyperactivity Disorder Electroencephalograms Based on Independent Component Analysis

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

In this paper, we present an automatic artifact removal method by combining independent component analysis (ICA) and wavelet denoising to remove artifacts from attention deficit hyperactivity disorder (ADHD) electroencephalograms (EEGs). First, the ICA algorithm is used to analyze EEG signals to get the independent components (ICs); then, the wavelet method is applied to the demixed ICs as an intermediate step; finally, the EEG data are reconstructed from the new ICs (that are obtained in the intermediate step) by using the inverse ICA. The experimental results show that the automatic artifact removal method can not only remove eye artifacts, muscle artifacts and some unknown physiological artifacts sources, but also efficiently keep the weak neural activities from strong background artifacts. The results can establish a basis for ADHD clinical diagnose study.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Spronk M, Jonkman LM, Kemner C. Response inhibition and attention processing in 5- to 7-year-old children with and without symptoms of ADHD: an ERP study. Clin Neurophysiol. 2008;119:2738–52.

    Article  PubMed  CAS  Google Scholar 

  2. Alexander DM, Hermens DF, Keage HA, Clark CR, Williams LM, Kohn MR, Kohn MR, Clarke SD, Lamb C, Gordon E. Event-related wave activity in the EEG provides new marker of ADHD. Clin Neurophysiol. 2008;119:163–79.

    Article  PubMed  Google Scholar 

  3. Madureira DQM, Carvalho LAV, Cheniaux E. Attentional focus modulated by mesothalamic dopamine: consequences in Parkinson’s disease and attention deficit hyperactivity disorder. Cogn Comput. 2010;2:31–49.

    Article  Google Scholar 

  4. Wiersema R, Van der Meere J, Roeyers H, Van Coster R, Baeyens D. Event rate and event-related potentials in ADHD. J Child Psychol Psychiatry. 2006;47:560–7.

    Article  PubMed  Google Scholar 

  5. Sikstrom S, Soderlund G. Stimulus-dependent dopamine release in attention-deficit/hyperactivity disorder. Psychol Rev. 2007;114:1047–75.

    Article  PubMed  Google Scholar 

  6. Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, Sejnowski TJ. Removing electroencephalographic artifacts by blind source separation. Psychophysiology. 2000;37(2):163–78.

    Article  PubMed  CAS  Google Scholar 

  7. Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage. 2007;34:1443–9.

    Article  PubMed  Google Scholar 

  8. Zhu Y, Shayan A, Zhang W, Chen TL, Jung TP, Duann JR, Makeig S, Cheng CK. Analyzing high-density ECG signals using ICA. IEEE Trans Biomed Eng. 2008;55:2528–37.

    Article  PubMed  Google Scholar 

  9. Zou L, Duan S, Ma Zh, Yang Ch. Single-trial event related potentials extraction by using independent component analysis. In: The 2nd international conference on biomedical engineering and informatics. 2009; pp. 721–5.

  10. Inuso G, La Foresta F, Mammone N, Morabito FC. Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings. In: Proceedings of IEEE IJCNN. 2007; pp. 1524–9.

  11. Demiralp T, Ademoglu A, Istefanopulos Y, Basar-Eroglu C, Basar E. Wavelet analysis of oddball P300. Int J Psychophysiol. 2001;39:221–7.

    Article  PubMed  CAS  Google Scholar 

  12. Zou L, Tao C, Zhang X, Zhou RL. Estimation of Event Related Potentials Using Wavelet Denoising Based Method. In: 7th International symposium on neural networks. 2010; vol. 4128, pp. 400–7.

  13. Zou L, Zhou R, Hu S, Zhang J, Li Y. Single trial evoked potentials study during an emotional processing based on wavelet transform. In: 5th International symposium on neural networks. 2008; vol. 5263, pp. 1–10.

  14. Akhtar MT, Mitsuhashi W, James CJ. Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. Signal Process. 2012;92:401–16.

    Article  Google Scholar 

  15. Ting KH, Fung PCW, Chang CQ, Chan FHY. Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only. Med Eng Phys. 2006;28:781–94.

    Article  Google Scholar 

  16. Belouchrani A, Abed-Meraim K, Cardoso J-F, Moulines E. A blind source separation technique based on second-order statistics. IEEE Trans Signal Process. 1997;45:434–44.

    Article  Google Scholar 

  17. Cardoso J-F. High-order contrasts for independent component analysis. Neural Comput. 1999;11:157–92.

    Article  PubMed  CAS  Google Scholar 

  18. Ruijiang L, Principe JC. Blinking artifact removal in cognitive EEG data using ICA. In: Proceedings of the 28th IEEE EMBS annual international conference, New York City, USA, Aug 30–Sept 3, 2006; pp. 5273–6.

  19. Lee T-W. Independent component analysis—theory and applications. Dordrecht: Kluwer Academic Publishers; 1998.

    Google Scholar 

  20. Jung T-P, Makeig S, Humphries C, Lee T-W, McKeown MJ, Iragui V, Sejnowski TJ. Removing electroencephalographic artifacts by blind source separation. Psychophysiology. 2000;37:163–78.

    Article  PubMed  CAS  Google Scholar 

  21. Donoho DL, Johnston IM. Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc. 1995;90:1200–24.

    Article  Google Scholar 

  22. Zou L, Zhang Y, Laurence TY, Zhou R. Single trial evoked potentials study by combining wavelet denoising and principal component analysis method. J Clin Neurophysiol. 2010;27:17–24.

    Article  PubMed  Google Scholar 

  23. Chang SG, Yu B, Vettreli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process. 2000;9:1532–46.

    Article  PubMed  CAS  Google Scholar 

  24. Polikar R, Topalis A, Green D, Kounios J, Clark CM. Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer’s disease. Comput Biol Med. 2007;37:542–56.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work has been partially supported by the open project of the State Key Laboratory of Robotics and System at Harbin Institute of Technology (SKLRS-2010-2D-09, SKLRS-2010-MS-10), National Natural Science Foundation of China (61201096), the Natural Science Fund for colleges and universities in Jiangsu Province (10KJB510003), the Natural Science Fund in Changzhou City (CJ20110023) and Changzhou High-tech Research Key Laboratory Project (CM20123006). Thanks Dr. Suhong Wang in the First Hospital of Changzhou City for helpful discussion.

Author information

Authors and Affiliations

  1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Ling Zou

  2. School of Information Science and Engineering, Changzhou University, Changzhou, 213164, Jiangsu, China

    Ling Zou, Soukun Xu, Zhenghua Ma & Jieru Lu

  3. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Wenjing Su

Authors
  1. Ling Zou
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Soukun Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhenghua Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jieru Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Wenjing Su
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhenghua Ma.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zou, L., Xu, S., Ma, Z. et al. Automatic Removal of Artifacts from Attention Deficit Hyperactivity Disorder Electroencephalograms Based on Independent Component Analysis. Cogn Comput 5, 225–233 (2013). https://doi.org/10.1007/s12559-012-9199-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-012-9199-3

Keywords