Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience / 2007 / Article
Special Issue

Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

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Research Article | Open Access

Volume 2007 |Article ID 082069 | https://doi.org/10.1155/2007/82069

Sebastian Halder, Michael Bensch, Jürgen Mellinger, Martin Bogdan, Andrea Kübler, Niels Birbaumer, Wolfgang Rosenstiel, "Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation", Computational Intelligence and Neuroscience, vol. 2007, Article ID 082069, 10 pages, 2007. https://doi.org/10.1155/2007/82069

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

Academic Editor: Andrzej Cichocki
Received16 Feb 2007
Revised31 May 2007
Accepted23 Aug 2007
Published12 Nov 2007

Abstract

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

References

  1. H. Berger, “Über das Elektroenzephalogramm des Menschen,” Archiv für Psychiatrie und Nervenkrankheiten, vol. 87, pp. 527–570, 1929. View at: Google Scholar
  2. J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002. View at: Publisher Site | Google Scholar
  3. D. J. McFarland, W. A. Sarnacki, T. M. Vaughan, and J. R. Wolpaw, “Brain-computer interface (BCI) operation: signal and noise during early training sessions,” Clinical Neurophysiology, vol. 116, no. 1, pp. 56–62, 2005. View at: Publisher Site | Google Scholar
  4. M. Iwasaki, C. Kellinghaus, A. V. Alexopoulos et al., “Effects of eyelid closure, blinks, and eye movements on the electroencephalogram,” Clinical Neurophysiology, vol. 116, no. 4, pp. 878–885, 2005. View at: Publisher Site | Google Scholar
  5. P. P. Caffier, U. Erdmann, and P. Ullsperger, “Experimental evaluation of eye-blink parameters as a drowsiness measure,” European Journal of Applied Physiology, vol. 89, no. 3-4, pp. 319–325, 2003. View at: Publisher Site | Google Scholar
  6. M. Fatourechi, A. Bashashati, R. K. Ward, and G. E. Birch, “EMG and EOG artifacts in brain-computer interface systems: a survey,” Clinical Neurophysiology, vol. 118, no. 3, pp. 480–494, 2007. View at: Publisher Site | Google Scholar
  7. J. S. Barlow, “EMG artifact minimization during clinic EEG recordings by special analog filtering,” Electroencephalography and Clinical Neurophysiology, vol. 58, no. 2, pp. 161–174, 1984. View at: Publisher Site | Google Scholar
  8. T. Elbert, W. Lutzenberger, B. Rockstroh, and N. Birbaumer, “Removal of ocular artifacts from the EEG—a biophysical approach to the EOG,” Electroencephalography and Clinical Neurophysiology, vol. 60, no. 5, pp. 455–463, 1985. View at: Publisher Site | Google Scholar
  9. R. J. Croft and R. J. Barry, “Removal of ocular artifact from the EEG: a review,” Neurophysiologie Clinique, vol. 30, no. 1, pp. 5–19, 2000. View at: Publisher Site | Google Scholar
  10. R. J. Croft and R. J. Barry, “Issues relating to the subtraction phase in EOG artefact correction of the EEG,” International Journal of Psychophysiology, vol. 44, no. 3, pp. 187–195, 2002. View at: Publisher Site | Google Scholar
  11. S. Makeig, A. J. Bell, T.-P. Jung, and T. J. Sejnowski, “Independent component analysis of electroencephalographic data,” in Advances in Neural Information Processing Systems, vol. 8, pp. 145–151, The MIT Press, Cambridge, Mass, USA, 1996. View at: Google Scholar
  12. R. N. Vigário, “Extraction of ocular artefacts from EEG using independent component analysis,” Electroencephalography and Clinical Neurophysiology, vol. 103, no. 3, pp. 395–404, 1997. View at: Publisher Site | Google Scholar
  13. T.-P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and T. J. Sejnowski, “Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects,” Clinical Neurophysiology, vol. 111, no. 10, pp. 1745–1758, 2000. View at: Publisher Site | Google Scholar
  14. C. A. Joyce, I. F. Gorodnitsky, and M. Kutas, “Automatic removal of eye movement and blink artifacts from EEG data using blind component separation,” Psychophysiology, vol. 41, no. 2, pp. 313–325, 2004. View at: Publisher Site | Google Scholar
  15. S. Romero, M. A. Mañanas, S. Clos, S. Gimenez, and M. J. Barbanoj, “Reduction of EEG artifacts by ICA in different sleep stages,” in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '03), vol. 3, pp. 2675–2678, Cancun, Mexico, September 2003. View at: Publisher Site | Google Scholar
  16. L. Tong, R.-W. Liu, V. C. Soon, and Y.-F. Huang, “Indeterminacy and identifiability of blind identification,” IEEE Transactions on Circuits and Systems, vol. 38, no. 5, pp. 499–509, 1991. View at: Publisher Site | Google Scholar
  17. J.-F. Cardoso, “High-order contrasts for independent component analysis,” Neural Computation, vol. 11, no. 1, pp. 157–192, 1999. View at: Publisher Site | Google Scholar
  18. S. Makeig, T.-P. Jung, A. J. Bell, D. Ghahremani, and T. J. Sejnowski, “Blind separation of auditory event-related brain-responses into independent components,” Proceedings of the National Academy of Sciences of the United States of America, vol. 94, no. 20, pp. 10979–10984, 1997. View at: Publisher Site | Google Scholar
  19. A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, no. 7, pp. 1483–1492, 1997. View at: Publisher Site | Google Scholar
  20. A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” Journal of Neuroscience Methods, vol. 134, no. 1, pp. 9–21, 2004. View at: Publisher Site | Google Scholar
  21. A. Cichocki, S. Amari, K. Siwek et al., “ICALAB Toolboxes,” http://www.bsp.brain.riken.jp/ICALAB. View at: Google Scholar
  22. H. H. Jasper, “The 10-20 electrode system of the international federation,” Electroencephalography and Clinical Neurophysiology, vol. 10, pp. 371–375, 1958. View at: Google Scholar
  23. G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, “BCI2000: a general-purpose brain-computer interface (BCI) system,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034–1043, 2004. View at: Publisher Site | Google Scholar
  24. N. J. Knight, Signal fraction analysis and artifact removal in EEG, M.S. thesis, Colorado State University, Fort Collins, Colo, USA, 2004, http://www.cs.colostate.edu/eeg/publications/natethesis.pdf.
  25. A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Networks, vol. 10, no. 3, pp. 626–634, 1999. View at: Publisher Site | Google Scholar
  26. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm. View at: Google Scholar
  27. P. D. Welch, “The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms,” IEEE Transactions on Audio and Electroacoustics, vol. 15, no. 2, pp. 70–73, 1967. View at: Publisher Site | Google Scholar
  28. S. Arimoto, “An algorithm for computing the capacity of arbitrary discrete memoryless channels,” IEEE Transactions on Information Theory, vol. 18, no. 1, pp. 14–20, 1972. View at: Publisher Site | Google Scholar
  29. R. E. Blahut, “ Computation of channel capacity and rate-distortion functions,” IEEE Transactions on Information Theory, vol. 18, no. 4, pp. 460–473, 1972. View at: Publisher Site | Google Scholar
  30. T.-F. Wu, C.-J. Lin, and R. C. Weng, “Probability estimates for multi-class classification by pairwise coupling,” Journal of Machine Learning Research, vol. 5, pp. 975–1005, 2004. View at: Google Scholar
  31. A. Kübler, F. Nijboer, J. Mellinger et al., “Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface,” Neurology, vol. 64, no. 10, pp. 1775–1777, 2005. View at: Google Scholar
  32. W. De Clercq, A. Vergult, B. Vanrumste, W. Van Paesschen, and S. Van Huffel, “Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, part 1, pp. 2583–2587, 2006. View at: Publisher Site | Google Scholar
  33. A. Delorme, T. Sejnowski, and S. Makeig, “Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis,” NeuroImage, vol. 34, no. 4, pp. 1443–1449, 2007. View at: Publisher Site | Google Scholar
  34. A. Schlögl, C. Keinrath, D. Zimmermann, R. Scherer, R. Leeb, and G. Pfurtscheller, “A fully automated correction method of EOG artifacts in EEG recordings,” Clinical Neurophysiology, vol. 118, no. 1, pp. 98–104, 2007. View at: Publisher Site | Google Scholar
  35. L. Shoker, S. Sanei, and J. Chambers, “Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm,” IEEE Signal Processing Letters, vol. 12, no. 10, pp. 721–724, 2005. View at: Publisher Site | Google Scholar
  36. N. Ille, P. Berg, and M. Scherg, “Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies,” Journal of Clinical Neurophysiology, vol. 19, no. 2, pp. 113–124, 2002. View at: Publisher Site | Google Scholar

Copyright © 2007 Sebastian Halder et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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