Skip to main content
SpringerLink
Log in

Multi-class Classification of EEG Spectral Data for Artifact Detection

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

Included in the following conference series:

Abstract

Electroencephalographic signals are known to be highly sensitive to various types of noise originating from external and internal sources. External sources are usually related to experiment conditions whereas internal artifacts are usually generated by the persons examined. Internal artifacts, in contrast to external ones, are characterised by non-stationarity, which results in higher detection complexity. Typical internal artifacts are related to eye blinking, eye movement and muscle activity.

The paper presents the comparison results of various approaches to classification of EEG-related features. Another aspect reviewed in the paper is comparing normalisation methods of dealing with inter-subject variability.

The analysis process covered several parts. Preprocessing included signal filtering and bad-channel removal. Signal epoching with 50% overlap was applied in order to achieve better time resolution. Feature extraction was based on frequency analysis performed with the Welch method. Due to the high number of obtained features, the feature selection procedure was the essential part of the processing. Selected features were used to train and validate supervised classifiers. The accuracy was the main measure used in classifier performance assessment.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Askamp, J., Putten, M.: Mobile EEG in epilepsy. Int. J. Psychophysiol. 91(1), 30–35 (2013)

    Article  Google Scholar 

  2. De Vos, M., Kroesen, M., Emkes, R., Debener, S.: P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier. J. Neural Eng. 11(3), 036008 (2014)

    Article  Google Scholar 

  3. Michel, C.M., et al.: Microstates in resting-state EEG: current status and future directions. Neurosci. Biobehav. Rev. 49, 105–113 (2014)

    Google Scholar 

  4. Lance, B.J., Kerick, S.E., Ries, A.J., Oie, K.S., McDowell, K.: Brain-computer interface technologies in the coming decades. Proc. IEEE 100, 1585–1599 (2012)

    Article  Google Scholar 

  5. Comani, S., et al.: Monitoring neuro-motor recovery from stroke with high-resolution EEG, robotics and virtual reality: a proof of concept. IEEE Trans. Neural Syst. Rehabil. Eng. 23(6), 1106–1116 (2015)

    Article  Google Scholar 

  6. Bissoli, A.L.C., Sime, M.M., Bastos-Filho, F.B.: Using sEMG, EOG and VOG to control an intelligent environment. IFAC-PapersOnLine 49(30), 210–215 (2016)

    Article  MathSciNet  Google Scholar 

  7. Di Fronso, S., et al.: Neural markers of performance states in an olympic athlete: an EEG case study in air-pistol shooting. J. Sport. Sci. Med. 15(15), 214–222 (2016)

    Google Scholar 

  8. Niedermeyer, E., Da Silva, F.: Electroencephalography, Basic Principals, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, Philadelphia (2005)

    Google Scholar 

  9. Lopez-Gordo, M.A., Sanchez-Morillo, D., Pelayo Valle, F.: Dry EEG electrodes. Sensors 14, 12847–12870 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Goncharova, I., Vaughanet, M.T., Mcfarland, D., Wolpaw, J.: EMG contamination of EEG: spectral and topographical characteristics. Clin. Neurophysiol. 114(9), 1580–1593 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Jung, T., et al.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37(2), 163–168 (2000)

    Article  Google Scholar 

  15. Lins, O.G., Picton, T.W., Berg, P., Scherg, M.: Ocular artifacts in recording EEG and event-related potentials. II: source dipoles and source components. Brain Topogr. 6(1), 65–78 (1993)

    Article  Google Scholar 

  16. Tamburro, G., Fiedler, P., Stone, D., Haueisen, J., Comani, S.: A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings. PeerJ 6, e4380 (2018)

    Article  Google Scholar 

  17. Halder, S. et al.: Online artifact removal for brain-computer interfaces using support vector machines and blind source separation. Comput. Intell. Neurosci. 1, 10–16 (2007)

    Google Scholar 

  18. Lawhern, V., et al.: Detection and classification of subject-generated artifacts in EEG signals using autoregressive models. J. Neurosci. Methods 2, 181–189 (2012)

    Article  Google Scholar 

  19. Goh, S.K., et al.: Automatic EEG artifact removal techniques by detecting influential independent components. IEEE Trans. Emerg. Top. Comput. Intell. 1, 270–279 (2017)

    Article  Google Scholar 

  20. Wang, D., Miao, D., Blohm, G.: Multi-class motor imagery EEG decoding for brain-computer interfaces. Front. Neurosci. 6, 151 (2012)

    Article  Google Scholar 

  21. Nolan, H., Whelan, R., Reilly, R.B.: FASTER: fully automated statistical thresholding for EEG artifact rejection. J. Neurosci. Methods 192(1), 152–162 (2010)

    Article  Google Scholar 

  22. Plechawska-Wójcik, M., et al.: Classifying cognitive workload based on brain waves signal in the arithmetic tasks’ study. In: 2018 11th International Conference on Human System Interaction (HSI), pp. 277–283. IEEE (2018)

    Google Scholar 

  23. Parvinnia, E., Sabeti, M., Zolghadri Jahromi, M., Boostani, R.: Classification of EEG signals using adaptive weighted distance nearest neighbor algorithm. J. King Saud Univ. – Comput. Inf. Sci. 26(1), 1–6 (2014)

    Article  Google Scholar 

  24. Lee, K., et al.: A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers. Robot. Auton. Syst. 90(c), 15–23 (2017)

    Article  Google Scholar 

  25. Liang, N., Bougrain, L.: Decoding finger flexion from band-specific ECoG signals in humans. Front. Neurosci. 6, 6–91 (2012)

    Article  Google Scholar 

  26. Lotte, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10-year update. J. Neural Eng. 15(3), 031005 (2018)

    Article  Google Scholar 

  27. Gu, Q., Li, Z., Han, J.: Generalized fisher score for feature selection. In: Proceeding UAI 2011 Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, pp. 266–273 (2011)

    Google Scholar 

  28. Dat T. H., Guan, C.: Feature selection based on fisher ratio and mutual information analyses for robust brain computer interface. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP 2007. IEEE (2007)

    Google Scholar 

  29. Roffo, G., Melzi, S.: Ranking to learn: feature ranking & selection via eigenvector centrality. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds.) New Frontiers in Mining Complex Patterns, pp. 1–15. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-61461-8_2

    Chapter  Google Scholar 

  30. Henni, K., Mezghani, N., Gouin-Vallerand, C.: Unsupervised graph-based feature selection via subspace and pagerank centrality. Expert Syst. Appl. 114, 46–53 (2018)

    Article  Google Scholar 

  31. Estévez, P.A., Tesmer, M., Perez, A., Zurada, J.M.: Normalized mutual information feature selection. IEEE Trans. Neural Netw. 20(2), 189–201 (2009)

    Article  Google Scholar 

  32. Bennasar, M., Hicks, Y., Setchi, R.: Feature selection using joint mutual information maximisation. Expert Syst. Appl. 42(22), 8520–8532 (2015)

    Article  Google Scholar 

  33. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  34. https://www.mathworks.com/matlabcentral/fileexchange/56937-feature-selection-library. Accessed 18 Dec 2018

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618, Lublin, Poland

    Mikhail Tokovarov, Małgorzata Plechawska-Wójcik & Monika Kaczorowska

Authors
  1. Mikhail Tokovarov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Małgorzata Plechawska-Wójcik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monika Kaczorowska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mikhail Tokovarov .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tokovarov, M., Plechawska-Wójcik, M., Kaczorowska, M. (2019). Multi-class Classification of EEG Spectral Data for Artifact Detection. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20915-5_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation