Skip to main content
SpringerLink
Log in

Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA)

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Electroencephalography (EEG) is almost contaminated with many artifacts while recording the brain signal activity. Clinical diagnostic and brain computer interface applications frequently require the automated removal of artifacts. In digital signal processing and visual assessment, EEG artifact removal is considered to be the key analysis technique. Nowadays, a standard method of dimensionality reduction technique like independent component analysis (ICA) and wavelet transform combination can be explored for removing the EEG signal artifacts. Manual artifact removal is time-consuming; in order to avoid this, a novel method of wavelet ICA (WICA) using fuzzy kernel support vector machine (FKSVM) is proposed for removing and classifying the EEG artifacts automatically. Proposed method presents an efficient and robust system to adopt the robotic classification and artifact computation from EEG signal without explicitly providing the cutoff value. Furthermore, the target artifacts are removed successfully in combination with WICA and FKSVM. Additionally, proposes the various descriptive statistical features such as mean, standard deviation, variance, kurtosis and range provides the model creation technique in which the training and testing the data of FKSVM is used to classify the EEG signal artifacts. The future work to implement various machine learning algorithm to improve performance of the system.

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

Similar content being viewed by others

Notes

  1. The best possible (optimal) hyperplane was calculated by finding the quadratic equation by using the Kuhn–Tucker.

References

  • Belousov AI, Verzakov SA, von Frese J (2002) A flexible classification approach with optimal generalization performance: support vector machines. Chemometr Intell Lab Syst 64:15–25

    Article  Google Scholar 

  • Blanco S, Garcia H, Quiroga RQ, Romanelli L, Rosso OA (1995) Stationarity of the EEG series. IEEE Eng Med Biol Soc 14:395–399

    Article  Google Scholar 

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27

    Article  Google Scholar 

  • Chang CQ, Chan FH, Ting KH, Fung PC (2006) Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only. Med Eng Phys 28(8):780–794

    Article  Google Scholar 

  • de Beer NA, van de Velde M, Cluitmans PJ (1995) Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring. J Clin Monit 11:381–391

    Article  Google Scholar 

  • Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Devipriya A, Nagarajan N (2018) A novel method of segmentation and classification for meditation in health care systems. J Med Syst 42(10):193

    Article  Google Scholar 

  • Gallois P, Vasseur C, Boudet S, Peyrodie L (2006) A global approach for automatic artifact removal for standard EEG record. In: Proceedings of IEEE engineering in Medicine and Biology Society (EMBC 2006), Orlando, FL, pp 5719–5722

  • Gotman J, Skuce DR, Thompson CJ, Gloor P, Ives JR, Ray WF (1973) Clinical applications of spectral analysis and extraction of features from electroencephalograms with slow waves in adult patients. Electroencephalogr Clin Neurophysiol 35:225–235

    Article  Google Scholar 

  • Guerrero-Mosquera C, Navia-Vazquez A (2012) Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data. IET Signal Proc 6:99–106

    Article  MathSciNet  Google Scholar 

  • Hjorth B (1975) Time domain descriptors and their relation to a particular model for generation of EEG activity. In: Dolce G, Künkel H (eds) CEAN: computerized EEG analysis. Gustav Fischer-Verlag, Stuttgart, pp 3–8

    Google Scholar 

  • Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V et al (2000) Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37:163–178

    Article  Google Scholar 

  • Lee T-W (1998) Independent component analysis—theory and applications. Kluwer Academic Publishers, New York

    Book  Google Scholar 

  • Lin C, Wang S (2004) Training algorithms for fuzzy support vector machines with noisy data. Pattern Recogn Lett 25:1647–1656

    Article  Google Scholar 

  • Lu Z, Li Y, Li Y, Ma Z (2006) Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiol Meas 27(4):425–436

    Article  Google Scholar 

  • Mahajan R, Morshed BI (2015) Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. IEEE J Biomed Health Inform 19:158–165

    Article  Google Scholar 

  • Makarov VA, Castellanos NP (2006) Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods 158(2):300–312

    Article  Google Scholar 

  • Mammone N, Morabito FC, Inuso G, La Foresta F (2007) Wavelet-ICA methodology for efficient artifact removal from electroencephalographic recordings. In: Proceedings of international joint conference neural networks (IJCNN 2007), Orlando, FL, pp 1524–1529

  • Mammone N, Foresta FL, Morabito FC (2012) Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. IEEE Sens J 12:533–542

    Article  Google Scholar 

  • Mamun M, Al-Kadi M, Marufuzzaman M (2013) Effectiveness of wavelet denoising on electroencephalogram signals. J Appl Res Technol 11:156–160

    Article  Google Scholar 

  • Renyi A (1960) On measures of information and entropy. In: Proceedings of 4th Berkeley symposium on mathematical statistics and probability, pp 547–561

  • Sabeti M, Katebi S, Boostani R (2009) Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif Intell Med 47(3):263–274

    Article  Google Scholar 

  • Shoker L, Sanei S, Chambers J (2005) Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Process Lett 12:721–724

    Article  Google Scholar 

  • Ten Daubechies I (1992) Lectures on wavelets. Society for Industrial and Applied Mathematics, Philadelphia

    Google Scholar 

  • Woestenburg JC, Verbaten MN, Slangen JL (1983) The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain. Biol Psychol 16:127–147

    Article  Google Scholar 

  • Zhang JH, Janschek K, Bohme JF, Zeng YJ (2004) Multi-resolution dyadic wavelet denoising approach for extraction of visual evoked potentials in the brain. IEE Proc Vis Image Signal Process 151:180–186

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, SNS College of Technology, Coimbatore, India

    K. Yasoda

  2. School of Computing, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamilnadu, India

    R. S. Ponmagal

  3. Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamilnadu, India

    K. S. Bhuvaneshwari

  4. School of CSE, Vellore Institute of Technology University Bhopal, Bhopal, India

    K. Venkatachalam

Authors
  1. K. Yasoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. S. Ponmagal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. S. Bhuvaneshwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. Venkatachalam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Venkatachalam.

Ethics declarations

Conflict of interest

All author states that there is no conflict of interest.

Human and animals rights statement

Humans/animals are not involved in this work.

Additional information

Communicated by V. Loia.

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

Yasoda, K., Ponmagal, R.S., Bhuvaneshwari, K.S. et al. Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA). Soft Comput 24, 16011–16019 (2020). https://doi.org/10.1007/s00500-020-04920-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04920-w

Keywords

Search

Navigation