Segmentation and Labelling of EEG for Brain Computer Interfaces

  • Tracey A. CamilleriEmail author
  • Kenneth P. Camilleri
  • Simon G. Fabri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


Segmentation and labelling of time series is a common requirement for several applications. A brain computer interface (BCI) is achieved by classification of time intervals of the electroencephalographic (EEG) signal and thus requires EEG signal segmentation and labelling. This work investigates the use of an autoregressive model, extended to a switching multiple modelling framework, to automatically segment and label EEG data into distinct modes of operation that may switch abruptly and arbitrarily in time. The applicability of this approach to BCI systems is illustrated on an eye closure dependent BCI and on a motor imagery based BCI. Results show that the proposed autoregressive switching multiple model approach offers a unified framework of detecting multiple modes, even in the presence of limited training data.


Motor Imagery Brain Computer Interface Intentional Control Brain Computer Interface System Limited Training Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Camilleri, T., Camilleri, K., Fabri, S.: Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models. Biomedical Signal Processing and Control 10, 117–127 (2014)CrossRefGoogle Scholar
  2. 2.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. R. Stat. Soc. 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Fabri, S.G., Kadirkamanathan, V.: Functional Adaptive Control - An Intelligent Systems Approach. Springer (2001)Google Scholar
  4. 4.
    Ghahramani, Z., Hinton, G.E.: Parameter Estimation for Linear Dynamical Systems. Technical report CRG-TR-96-2, Department of Computer Science, University of Toronto (1996)Google Scholar
  5. 5.
    Hoffman, G.S.: A Novel Electocardiogram Segmentation Algorithm using a Multiple Model Adaptive Estimator. Master’s thesis, Air Force Institute of Technology, Graduate School of Engineering and Management, Ohio (2002)Google Scholar
  6. 6.
    Maybeck, P.S.: Stochastic Models, Estimation and Control. Mathematics in Science and Engineering. Academic Press Inc., London (1979)Google Scholar
  7. 7.
    Maybeck, P., Stevens, R.: Reconfigurable flight control via multiple model adaptive control methods. IEEE Trans. on Aerosp. and Electron. Syst. 27(3), 470–480Google Scholar
  8. 8.
    Pardey, J., Roberts, S., Tarassenko, L.: A Review of Parametric Modelling Techniques for EEG Analysis. Med. Eng. Phys. 18, 2–11 (1996)CrossRefGoogle Scholar
  9. 9.
    Sajda, P., Gerson, A., Muller, K.R., Blankertz, B., Parra, L.: A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 184–185 (2003)CrossRefGoogle Scholar
  10. 10.
    Sanei, S., Chambers, J.: EEG Signal Processing. John Wiley & Sons, Inc. (2007)Google Scholar
  11. 11.
    Schomer, D.L., Lopes da Silva, F.H. (eds.): Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 6th edn. Lippincott Williams & Wilkins (2010)Google Scholar
  12. 12.
    Thuraisingham, R.A., Tram, Y., Boord, P.A.C.: Analysis of eyes open, eye closed EEG signals using second-order difference plot. Medical and Biomedical Engineering and Computing 45, 1243–1249 (2007)CrossRefGoogle Scholar
  13. 13.
    Townsend, G., Graimann, B., Pfurtscheller, G.: Continuous EEG classification during motor imagery-simulation of an asynchronous BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 12(2), 258–265 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tracey A. Camilleri
    • 1
    Email author
  • Kenneth P. Camilleri
    • 1
  • Simon G. Fabri
    • 1
  1. 1.Department of Systems and Control EngineeringUniversity of MaltaMsidaMalta

Personalised recommendations