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Ballistocardiogram Artifact Removal for Concurrent EEG-fMRI Recordings Using Blind Source Separation Based on Dictionary Learning

  • Yuxi Liu
  • Jianhai Zhang
  • Bohui ZhangEmail author
  • Wanzeng Kong
Conference paper
  • 45 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 581)

Abstract

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have attracted extensive attention and research owing to their high spatial and temporal resolution. However, EEG data are easily influenced by physiological causes, gradient artifact (GA) and ballistocardiogram (BCG) artifact. In this paper, a new blind source separation technique based on dictionary learning is proposed to remove BCG artifact. The dictionary is learned from original data which represents the features of clean EEG signals and BCG artifact. Then, the dictionary atoms are classified according to a list of standards. Finally, clean EEG signals are obtained from the linear combination of the modified dictionary. The proposed method, ICA, AAS, and OBS are tested and compared using simulated data and real simultaneous EEG–fMRI data. The results suggest the efficacy and advantages of the proposed method in the removal of BCG artifacts.

Keywords

Eelectroencephalography (EEG) functional Magnetic Resonance Imaging (fMRI) Ballistocardiogram Dictionary learning Signal processing 

Notes

Acknowledgement

This work was supported by NSFC (61633010, 61671193, 61602140), National Key Research & Development Project (2017YFE0116800), Key Research & Development Project of Zhejiang Province (2020C04009, 2018C04012).

References

  1. 1.
    Mulert, C., Pogarell, O., Hegerl, U.: Simultaneous EEG-fMRI: perspectives in psychiatry. CEN 39(2), 61–64 (2008).  https://doi.org/10.1177/155005940803900207CrossRefGoogle Scholar
  2. 2.
    Shams, N., Alain, C., Strother, S.: Comparison of BCG artifact removal methods for evoked responses in simultaneous EEG–fMRI. J. Neurosci. Methods 245, 137–146 (2015)CrossRefGoogle Scholar
  3. 3.
    Iannotti, G.R., Pittau, F., Michel, C.M., Vulliemoz, S., Grouiller, F.: Pulse artifact detection in simultaneous EEG-fMRI recording based on EEG map topography. Brain Topogr. 28(1), 21–32 (2015)CrossRefGoogle Scholar
  4. 4.
    Allen, P.J., Polizzi, G., Krakow, K., Fish, D.R., Lemieux, L.: Identification of EEG events in the MR scanner: the problem of pulse artifact and a method for its subtraction. Neuroimage 8(3), 229–239 (1998)CrossRefGoogle Scholar
  5. 5.
    Bénar, C., Aghakhani, Y., Wang, Y., et al.: Quality of EEG in simultaneous EEG–fMRI for epilepsy. Clin. Neurophysiol. 114(3), 569–580 (2003)CrossRefGoogle Scholar
  6. 6.
    Niazy, K., Beckmann, C.F., Iannetti, G.D., et al.: Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28(3), 720–737 (2005)CrossRefGoogle Scholar
  7. 7.
    Hu, L., Zhang, Z.: EEG Signal Processing and Feature Extraction. Springer, Singapore (2019).  https://doi.org/10.1007/978-981-13-9113-2
  8. 8.
    Abolghasemi, V., Ferdowsi, S.: EEG–fMRI: dictionary learning for removal of ballistocardiogram artifact from EEG. Biomed. Signal Process. Control 18, 186–194 (2015)CrossRefGoogle Scholar
  9. 9.
    Ghaderi, F., Nazarpour, K., Mcwhirter, J.G., et al.: Removal of ballistocardiogram artifacts using the cyclostationary source extraction method. IEEE Trans. Biomed. Eng. 57(11), 2667–2676 (2010)CrossRefGoogle Scholar
  10. 10.
    Mantini, D., Perrucci, M.G., Cugini, S., Ferretti, A., Romani, G.L., Del Gratta, C.: Complete artifact removal for EEG recorded during continuous fMRI using independent component analysis. Neuroimage 34, 598–607 (2007)CrossRefGoogle Scholar
  11. 11.
    Winkler, I., Haufe, S., Tangermann, M.: Automatic classification of artifactual ICA-components for artifact removal in EEG signals. BBF 7, Article no. 30 (2011).  https://doi.org/10.1186/1744-9081-7-30
  12. 12.
    de Munck, J.C., van Houdt, P.J., Gonçalves, S.I., van Wegen, E.E.H., Ossenblok, P.P.W.: Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction. NeuroImage 64, 407–415 (2013)Google Scholar
  13. 13.
    Quan, Y., Xu, Y., Sun, Y., Huang, Y., Ji, H.: Sparse coding for classification via discrimination ensemble. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5839–5847 (2016)Google Scholar
  14. 14.
    Demanuele, C., James, C.J., Sonuga-Barke, E.J.: Behav. Brain Funct. 3, 62 (2007).  https://doi.org/10.1186/1744-9081-3-62CrossRefGoogle Scholar
  15. 15.
  16. 16.
    Dressler, O., Schneider, G., Stockmanns, G., Kochs, E.F.: Awareness and the EEG power spectrum: analysis of frequencies. BJA 93, 806–809 (2004).  https://doi.org/10.1093/bja/aeh270CrossRefGoogle Scholar
  17. 17.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning - ICML 2009 (2009)Google Scholar
  18. 18.
    Liu, Z., de Zwart, J.A., van Gelderen, P., Kuo, L.-W., Duyn, J.: Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings. Neuroimage 59, 2073–2087 (2012)Google Scholar
  19. 19.
    Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  • Yuxi Liu
    • 1
  • Jianhai Zhang
    • 1
    • 2
  • Bohui Zhang
    • 3
    Email author
  • Wanzeng Kong
    • 1
    • 2
  1. 1.School of Computer ScienceHangzhou Dianzi UniversityHangzhouChina
  2. 2.Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang ProvinceHangzhouChina
  3. 3.University of Southern CaliforniaLos AngelesUSA

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