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BioMed Research International
Volume 2015, Article ID 720450, 8 pages
http://dx.doi.org/10.1155/2015/720450
Research Article

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China

Received 4 December 2014; Revised 13 March 2015; Accepted 18 March 2015

Academic Editor: Tsair-Fwu Lee

Copyright © 2015 Chi Zhang 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.

Linked References

  1. 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 · View at Google Scholar · View at Scopus
  2. 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 · View at Google Scholar · View at Scopus
  3. N. A. M. De Beer, M. van de Velde, and P. J. M. Cluitmans, “Clinical evaluation of a method for automatic detection and removal of artifacts in auditory evoked potential monitoring,” Journal of Clinical Monitoring, vol. 11, no. 6, pp. 381–391, 1995. View at Publisher · View at Google Scholar · View at Scopus
  4. G. Gratton, “Dealing with artifacts: the EOG contamination of the event-related brain potential,” Behavior Research Methods, Instruments, & Computers, vol. 30, no. 1, pp. 44–53, 1998. View at Publisher · View at Google Scholar · View at Scopus
  5. 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 · View at Google Scholar · View at Scopus
  6. S. Choi, A. Cichocki, H.-M. Park, and S.-Y. Lee, “Blind source separation and independent component analysis: a review,” Neural Information Processing—Letters and Reviews, vol. 6, no. 1, pp. 1–57, 2005. View at Google Scholar
  7. 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, pp. 145–151, 1996. View at Google Scholar
  8. 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 · View at Google Scholar · View at Scopus
  9. M. Crespo-Garcia, M. Atienza, and J. L. Cantero, “Muscle artifact removal from human sleep EEG by using independent component analysis,” Annals of Biomedical Engineering, vol. 36, no. 3, pp. 467–475, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Flexer, H. Bauer, J. Pripfl, and G. Dorffner, “Using ICA for removal of ocular artifacts in EEG recorded from blind subjects,” Neural Networks, vol. 18, no. 7, pp. 998–1005, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. X. Zhang, S. Qiu, Y. Ke et al., “Stimulus artifact removal of semg signals detected during functional electrical stimulation,” Biomedical Engineering, 2013. View at Publisher · View at Google Scholar
  12. M. Li, Y. Cui, and J. Yang, “Automatic removal of ocular artifact from EEG with DWT and ICA Method,” Applied Mathematics and Information Sciences, vol. 7, no. 2, pp. 809–816, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. H. P. Huang, Y. H. Liu, C. P. Wang, and T. H. Huang, “Automatic artifact removal in EEG using independent component analysis and one-class classification strategy,” Journal of Neuroscience and Neuroengineering, vol. 2, no. 2, pp. 73–78, 2013. View at Publisher · View at Google Scholar
  14. W.-Y. Hsu, C.-H. Lin, H.-J. Hsu, P.-H. Chen, and I.-R. Chen, “Wavelet-based envelope features with automatic EOG artifact removal: application to single-trial EEG data,” Expert Systems with Applications, vol. 39, no. 3, pp. 2743–2749, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. M. T. Akhtar and C. J. James, “Focal artifact removal from ongoing EEG—a hybrid approach based on spatially-constrained ICA and wavelet de-noising,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '09), vol. 2009, pp. 4027–4030, Minneapolis, Minn, USA, September 2009. View at Publisher · View at Google Scholar
  16. G. Bartels, L.-C. Shi, and B.-L. Lu, “Automatic artifact removal from EEG—a mixed approach based on double blind source separation and support vector machine,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '10), pp. 5383–5386, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Halder, M. Bensch, J. Mellinger et al., “Online artifact removal for brain-computer interfaces using support vector machines and blind source separation,” Computational Intelligence and Neuroscience, vol. 2007, Article ID 82069, 10 pages, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. C. J. James and O. J. Gibson, “Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 9, pp. 1108–1116, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. I. Daly, M. Billinger, R. Scherer, and G. Müller-Putz, “On the automated removal of artifacts related to head movement from the EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 3, pp. 427–434, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. I. Winkler, S. Haufe, and M. Tangermann, “Automatic classification of artifactual ICA-components for artifact removal in EEG signals,” Behavioral and Brain Functions, vol. 7, article 30, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. B. Azzerboni, M. Carpentieri, F. La Foresta, and F. C. Morabito, “Neural-ICA and wavelet transform for artifacts removal in surface EMG,” in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 4, pp. 3223–3228, July 2004. View at Scopus
  22. B. Azzerboni, G. Finocchio, M. Ipsale, F. La Foresta, and F. C. Morabito, “A new approach to detection of muscle activation by independent component analysis and wavelet transform,” in Neural Nets, vol. 2486 of Lecture Notes in Computer Science, pp. 109–116, Springer, Berlin, Germany, 2002. View at Publisher · View at Google Scholar
  23. G. Inuso, F. La Foresta, N. Mammone, and F. C. Morabito, “Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '07), pp. 1524–1529, Orlando, Fla, USA, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. X.-W. Wang, D. Nie, and B.-L. Lu, “Emotional state classification from EEG data using machine learning approach,” Neurocomputing, vol. 129, pp. 94–106, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Lu, M. Hui, and H. Yu-Xia, “The development of native Chinese affective picture system—a pretest in 46 college students,” Chinese Mental Health Journal, vol. 19, no. 11, pp. 719–722, 2005. View at Google Scholar
  26. D. Sammler, M. Grigutsch, T. Fritz, and S. Koelsch, “Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music,” Psychophysiology, vol. 44, no. 2, pp. 293–304, 2007. View at Publisher · View at Google Scholar · View at Scopus
  27. Y.-P. Lin, C.-H. Wang, T.-P. Jung et al., “EEG-based emotion recognition in music listening,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 7, pp. 1798–1806, 2010. View at Publisher · View at Google Scholar · View at Scopus