Advertisement

Optimal Feature Selection for Artifact Classification in EEG Time Series

  • Vernon Lawhern
  • W. David Hairston
  • Kay Robbins
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)

Abstract

Identifying artifacts or non-brain electrical signals in EEG time series is often a necessary but time-consuming preprocessing step, as many EEG analysis techniques require that the data be artifact free. Because of this, reliable and accurate techniques for automated artifact detection are desirable in practice. Previous research has shown that coefficients obtained from autoregressive (AR) models can be used as feature vectors to classify among several different artifact conditions found in EEG. However, a statistical method for identifying significant AR features has not been presented. In this work we propose a method for determining the optimal AR features that is based on penalized multinomial regression. Our results indicate that the size of the feature vector can be greatly reduced with minimal loss to classification accuracy. The features selected by this algorithm localize to specific channels and suggests a possible BCI implementation with increased computational efficiency than with using all available channels. We also show that the significant AR features produced by this approach correlate to known brain physiological properties.

Keywords

Autoregressive (AR) model Artifacts Electroencephalography classification feature selection multinomial regression penalized regression machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lin, C.-T., Chang, C.-J., Lin, B.-S., Hung, S.-H., Chao, C.-F., Wang, I.-J.: A Real-Time Wireless Brain-Computer Interface System for Drowsiness Detection. IEEE Transactions on Biomedical Circuits and Systems 4, 214–222 (2010)CrossRefGoogle Scholar
  2. 2.
    Lance, B.J., Kerick, S.E., Ries, A.J., Oie, K.S., McDowell, K.: Brain-Computer Interface Technologies in the Coming Decades. Proceedings of the IEEE 100, 1585–1599 (2012)CrossRefGoogle Scholar
  3. 3.
    Baccalá, L.A., Sameshima, K.: Partial directed coherence: a new concept in neural structure determination. Biol. Cybern. 84, 463–474 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Möller, E., Schack, B., Arnold, M., Witte, H.: Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models. J. Neurosci. Methods 105, 143–158 (2001)CrossRefGoogle Scholar
  5. 5.
    Franaszczuk, P.J., Bergey, G.K., Kamiński, M.J.: Analysis of mesial temporal seizure onset and propagation using the directed transfer function method. Electroencephalography and Clinical Neurophysiology 91, 413–427 (1994)CrossRefGoogle Scholar
  6. 6.
    Anderson, C.W., Stolz, E.A., Shamsunder, S.: Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. IEEE Transactions on Biomedical Engineering 45, 277–286 (1998)CrossRefGoogle Scholar
  7. 7.
    Ge, D., Srinivasan, N., Krishnan, S.M.: Cardiac arrhythmia classification using autoregressive modeling. BioMedical Engineering OnLine 1, 5 (2002)CrossRefGoogle Scholar
  8. 8.
    Übeyli, E.D.: Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert Systems with Applications 37, 233–239 (2010)CrossRefGoogle Scholar
  9. 9.
    Van de Velde, M., Ghosh, I.R., Cluitmans, P.J.M.: Context related artefact detection in prolonged EEG recordings. Computer Methods and Programs in Biomedicine 60, 183–196 (1999)CrossRefGoogle Scholar
  10. 10.
    Lawhern, V., Hairston, W.D., McDowell, K., Westerfield, M., Robbins, K.: Detection and classification of subject-generated artifacts in EEG signals using autoregressive models. J. Neurosci. Methods 208, 181–189 (2012)CrossRefGoogle Scholar
  11. 11.
    Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33, 1 (2010)Google Scholar
  12. 12.
    Zhu, J., Hastie, T.: Classification of gene microarrays by penalized logistic regression. Biostatistics 5, 427–443 (2004)zbMATHCrossRefGoogle Scholar
  13. 13.
    U.S. Department of Defense, Office of the Secretary of Defense: Code of federal regulations, protection of human subjects. Government Printing Office. 32 CFR 19 (1999)Google Scholar
  14. 14.
    U.S. Department of the Army: Use of volunteers as subjects of research. Government Printing Office. AR 70-25 (1990) Google Scholar
  15. 15.
    Schlögl, A.: A comparison of multivariate autoregressive estimators. Signal Processing 86, 2426–2429 (2006)zbMATHCrossRefGoogle Scholar
  16. 16.
    Kim, Y.S., Baek, H.J., Kim, J.S., Lee, H.B., Choi, J.M., Park, K.S.: Helmet-based physiological signal monitoring system. Eur. J. Appl. Physiol. 105, 365–372 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vernon Lawhern
    • 1
  • W. David Hairston
    • 2
  • Kay Robbins
    • 1
  1. 1.Department of Computer ScienceUniversity of Texas-San AntonioSan AntonioUSA
  2. 2.Human Research and Engineering DirectorateArmy Research LaboratoryUSA

Personalised recommendations