Removal of Ocular Artifacts from EEG Using Learned Templates

  • Max Quinn
  • Santosh Mathan
  • Misha Pavel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8027)


Electroencephalogram (EEG) data can provide information on cognitive states and processes with high temporal resolution, but to take full advantage of this temporal resolution, common transients such as blinks and eye movements must be accounted for without censoring data. This can require additional hardware, large amounts of data, or manual inspection. In this paper we introduce a greedy, template-based method for modeling and removing transient activity. The method iteratively models an input and updates a template; a process which quickly converges to a unique and efficient approximation of the input. When combined with standard source separation techniques such as Independent Component Analysis (ICA) or Principal Component Analysis (PCA), the method shows promise for the automatic and data driven removal of ocular artifacts from EEG data. In this paper we outline our method, provide evidence for its effectiveness using synthetic EEG data, and demonstrate its effect on real EEG data recorded as part of a minimally constrained cognitive task.


EEG EOG ICA PCA BCI matching pursuit 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fatourechi, M., Bashashati, A., Ward, R.K., Birch, G.E.: EMG and EOG Artifacts in Brain Computer Interface Systems: A Survey. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 118(3), 480–494 (2007)CrossRefGoogle Scholar
  2. 2.
    Croft, R.J.: The Removal of Ocular Artifact from the EEG. University of Wollongong (1999)Google Scholar
  3. 3.
    Parra, L.C., Spence, C.D., Gerson, A.D., Sajda, P.: Recipes for the Linear Analysis of EEG. NeuroImage 28(2), 326–341 (2005)CrossRefGoogle Scholar
  4. 4.
    Jung, T., Humphries, C., Makeig, S., Mckeown, M.J., Iragui, V., Sejnowski, T.J.: Extended ICA Removes Artifacts from Electroencephalographic Recordings. Neural Information Processing Systems 10, 894–900 (1998)Google Scholar
  5. 5.
    Jung, T., Humphries, C., Lee, T., Makeig, S., Mckeown, M.J., Iragui, V., Sejnowskil, T.J.: Removing Electroencephalographic Artifacts: Comparison Between ICA and PCA. Neural Networks for Signal Processing VIII. In: Proceedings of the 1998 IEEE Signal Processing Society Workshop, pp. 63–72 (1998)Google Scholar
  6. 6.
    Castellanos, N.P., Makarov, V.A.: Recovering EEG Brain Signals: Artifact Suppression with Wavelet Enhanced Independent Component Analysis. Journal of Neuroscience Methods 158, 300–312 (2006)CrossRefGoogle Scholar
  7. 7.
    Durka, P.J., Blinowska, K.J.: Analysis of EEG Transients by means of Matching Pursuit. Annals of Biomedical Engineering 23(5), 608–611 (1995)CrossRefGoogle Scholar
  8. 8.
    Mallat, S.G., Zhang, Z.: Matching Pursuits With Time-Frequency Dictionaries. IEEE Transactions on Signal Processing 41(12), 3397–3415 (1993)zbMATHCrossRefGoogle Scholar
  9. 9.
    Krstulovic, S., Gribonval, R.: Mptk: Matching Pursuit Made Tractable. In: ICASSP 2006 Proceedings, 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 3 (2006)Google Scholar
  10. 10.
    Mathan, S., Smart, A., Ververs, T., Feuerstein, M.: Towards an Index of Cognitive Efficacy. Engineering in Medicine and Biology (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Max Quinn
    • 1
  • Santosh Mathan
    • 2
  • Misha Pavel
    • 1
  1. 1.Oregon Health and Science UniversityPortlandUSA
  2. 2.Honeywell LaboratoriesRedmondUSA

Personalised recommendations