Analysis of Blind Source Separation Techniques for Eye Artifact Removal

  • Theus H. Aspiras
  • Vijayan K. Asari
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 292)

Abstract

Evaluation of several different eye artifact removal techniques for electroencephalographic data is presented in this paper. Data is taken from an emotion recognition experiment, in which subjects undergo five different emotions (joy, sadness, disgust, fear, and neutral). Preprocessing for the EEG Data includes filtering with a Butterworth band-pass filter and a 60Hz notch filter. Three different types of eye artifact removal techniques are explored using the preprocessed data: EOG based linear regression, Principal Component Analysis, and Independent Component Analysis. All techniques used electrooculographic (EOG) data to determine the criteria for feature extraction and removal. Evaluations from our experiments show that all techniques significantly reduce the effects of eye blinks and eye movements in the EEG. The developed metric used in experimentation shows that Independent Component Analysis reduced eye artifacts the best while keeping EEG portions unchanged (Average SSE of 0.1126 for clean EEG portions).

Keywords

Electroencephalography Electrooculography Eye Artifact Removal Independent Component Analysis Strength of Eye Blink 
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References

  1. 1.
    Dirlich, G., Vogl, L., Plaschke, M., Strian, F.: Cardiac Field Effects on the EEG. Electroencephalography and Clinical Neurophysiology 102, 307–315 (1997)CrossRefGoogle Scholar
  2. 2.
    Dewan, M., Hossain, M., Hoque, M., Chae, O.: Contaminated ECG Artifact Detection and Elimination from EEG using Energy Function Based Transformation. In: Information and Communication Technology, ICICT, pp. 52–56 (2007)Google Scholar
  3. 3.
    Narasimhan, S., Dutt, D.: Application of LMS Adaptive Predictive Filtering for Muscle Artifact (noise) Cancellation from EEG Signals. Computers and Electrical Engineering 22, 13–30 (1996)CrossRefGoogle Scholar
  4. 4.
    De Clercq, W., Vergult, A., Vanrumste, B., Van Hees, J., Palmini, A., Van Paesschen, W., Van Huffel, S.: A New Muscle Artifact Removal Technique to Improve the Interpretation of the Ictal Scalp Electroencephalogram. In: Engineering in Medicine and Biology Society, IEEE-EMBS, pp. 944–947 (2005)Google Scholar
  5. 5.
    Ferdjallah, M., Barr, R.: Adaptive Digital Notch Filter Design on the Unit Circle for the Removal of Powerline Noise from Biomedical Signals. IEEE Transactions Biomedical Engineering 41, 529–536 (1994)CrossRefGoogle Scholar
  6. 6.
    Jervis, B., Nichols, M., Allen, E., Hudson, N., Johnson, T.: The Assessment of Two Methods for Removing Eye Movement Artefact from the EEG. Electroencephalography and Clinical Neurophysiology 61, 444–452 (1985)CrossRefGoogle Scholar
  7. 7.
    Ramanan, S., Kalpakam, N., Sahambi, J.: A Novel Wavelet Based Technique for Detection and De-noising of Ocular Artifact in Normal and Epileptic Electroencephalogram. In: Communications, Circuits and Systems, ICCCAS, pp. 1027–1031 (2004)Google Scholar
  8. 8.
    Erfanian, A., Mahmoudi, B.: Real-Time Eye Blink Suppression using Neural Adaptive Filters for EEG-based Brain Computer Interface. In: 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, Engineering in Medicine and Biology, vol. 1, pp. 44–45 (2002)Google Scholar
  9. 9.
    Sovierzoski, M., Schwarz, L., Azevedo, F.: Binary Neural Classifier of Raw EEG Data to Separate Spike and Sharp Wave of the Eye Blink Artifact. Natural Computation, ICNC 2, 126–130 (2009)Google Scholar
  10. 10.
    Selvan, S., Srinivasan, R.: Removal of Ocular Artifacts from EEG using an Efficient Neural Network based Adaptive Filtering Technique. IEEE Signal Processing Letters 6, 330–332 (1999)CrossRefGoogle Scholar
  11. 11.
    Gomez-Herrero, G., De Clercq, W., Anwar, H., Kara, O., Egiazarian, K., Van Huffel, S., Van Paesschen, W.: Automatic Removal of Ocular Artifacts in the EEG without an EOG reference channel. In: Signal Processing Symposium, NORSIG, pp. 130–133 (2006)Google Scholar
  12. 12.
    Liu, T., Yao, D.: Removal of the Ocular Artifacts from EEG Data using a Cascaded Spatio-Temporal Processing. Computer Methods and Programs in Biomedicine 83, 95–103 (2006)CrossRefGoogle Scholar
  13. 13.
    Zhou, W., Zhou, J., Zhao, H., Ju, L.: Removing Eye Movement and Power Line Artifacts from the EEG based on ICA. In: 27th Annual International Conference on Engineering in Medicine and Biology Society, IEEE-EMBS, pp. 6017–6020 (2005)Google Scholar
  14. 14.
    Zhou, W., Gotman, J.: Automatic Removal of Eye Movement Artifacts from the EEG using ICA and the Dipole Model. Progress in Natural Science 19, 1165–1170 (2009)CrossRefGoogle Scholar
  15. 15.
    Flexer, A., Bauer, H., Pripfl, J., Dorffner, G.: Using ICA for removal of Ocular Artifacts in EEG recorded from Blind Subjects. Neural Networks 18, 998–1005 (2005)CrossRefGoogle Scholar
  16. 16.
    Klados, M., Papadelis, C., Bamidis, P.: Reg-ica: A New Hybrid Method for EOG Artifact Rejection. In: 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009, pp. 1–4 (2009)Google Scholar
  17. 17.
    Li, R., Principe, J.: Blinking Artifact Removal in Cognitive EEG data using ICA. In: 28th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS, pp. 5273–5276 (2006)Google Scholar
  18. 18.
    Aspiras, T.H., Asari, V.K.: Analysis of Spatio-temporal Relationship of Multiple Energy Spectra of EEG Data for Emotion Recognition. In: Venugopal, K.R., Patnaik, L.M. (eds.) ICIP 2011. CCIS, vol. 157, pp. 572–581. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Aspiras, T.H., Asari, V.K.: Log Power Representation of EEG Spectral Bands for the Recognition of Emotional States of Mind. In: 8th International Conference on Information Communications and Signal Processing, ICICS, pp. 1–5 (2011)Google Scholar
  20. 20.
    Obradovic, D., Deco, G.: Blind Source Separation: Are Information Maximization and Redundancy Minimization Different? In: Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing VII, pp. 416–425 (1997)Google Scholar
  21. 21.
    Hyvarinen, A.: Fast ICA for Noisy Data using Gaussian Moments. In: Proceedings of the 1999 IEEE International Symposium on Circuits and Systems, vol. 5, pp. 57–61 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Theus H. Aspiras
    • 1
  • Vijayan K. Asari
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of DaytonDaytonUSA

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