Random Segmentation Based Principal Component Analysis to Remove Residual MR Gradient Artifact in the Simultaneous EEG/fMRI: A Preliminary Study

  • Hyun-Chul Kim
  • Jong-Hwan Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)


In the electroencephalography (EEG) data simultaneously acquired with the functional magnetic resonance imaging (fMRI) data, the removal of the residual magnetic resonance (MR) gradient artifacts has been a challenging issue. To remove gradient artifacts generated from switching MR gradient field, average artifact subtraction (AAS) has been widely used. After applying the AAS method, however, residual MR gradient artifacts still remained in corrected EEG data. In this study, we proposed a novel method to remove the residual MR gradient artifacts (GAs) using random segmentation based principal component analysis (rsPCA). The performance of rsPCA was compared to that of the independent component analysis (ICA) method using data acquired from a motor imagery task. The results indicated that rsPCA could suppress further the residual MR gradient artifacts remained from the AAS step compared to the ICA method.


Simultaneous EEG/fMRI random segmentation principal component analysis electroencephalography functional magnetic resonance imaging MR gradient artifact 


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  1. 1.
    Horwitz, B., Poeppel, D.: How Can EEG/MEG and fMRI/PET Data Be Combined? Hum. Brain. Mapp. 17, 1–3 (2002)CrossRefGoogle Scholar
  2. 2.
    Debener, S., Mullinger, K.J., Niazy, R.K., Bowtell, R.W.: Properties of the Ballistocardiogram Artefact as Revealed by EEG Recordings at 1.5, 3 and 7 T Static Magnetic Field Strength. Int. J. Psychophysiol. 67, 189–199 (2008)CrossRefGoogle Scholar
  3. 3.
    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, 229–239 (1998)CrossRefGoogle Scholar
  4. 4.
    Allen, P.J., Josephs, O., Turner, R.: A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI. NeuroImage 12, 230–239 (2000)CrossRefGoogle Scholar
  5. 5.
    Niazy, R.K., Beckmann, C.F., Iannetti, G.D., Brady, J.M., Smith, S.M.: Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage 28, 720–737 (2005)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Lee, J.H., Oh, S., Jolesz, F.A., Park, H.W., Yoo, S.S.: Application of Independent Component Analysis for the Data Mining of Simultaneous EEG-fMRI: Preliminary Experience on Sleep Onset. Int. J. Neurosci. 119, 1118–1136 (2009)CrossRefGoogle Scholar
  8. 8.
    Gonçalves, S.I., Pouwels, P.J.W., Kuijer, J.P.A., Heethaar, R.M., de Munck, J.C.: Artifact Removal in Co-registered EEG/fMRI by Selective Average Subtraction. Clin. Neurophysiol. 118, 2437–2450 (2007)CrossRefGoogle Scholar
  9. 9.
    Garreffa, G., Carnì, M., Gualniera, G., Ricci, G.B., Bozzao, L., De Carli, D., Morasso, P., Pantano, P., Colonnese, C., Roma, V., Maraviglia, B.: Real-Time MR Artifacts Filtering during Continuous EEG/fMRI Acquisition. Magn. Reson. Imaging 21, 1175–1189 (2003)CrossRefGoogle Scholar
  10. 10.
    Negishi, M., Abildgaard, M., Nixon, T., Constable, R.T.: Removal of Time-Varying Gradient Artifacts from EEG Data Acquired during Continuous fMRI. Clinical Neurophysiol. 115, 2181–2192 (2004)CrossRefGoogle Scholar
  11. 11.
    Liu, Z., de Zwart, J.A., van Gelderen, P., Kuo, L.W., Duyn, J.H.: Statistical Feature Extraction for Artifact Removal from Concurrent fMRI-EEG Recordings. NeuroImage 59, 2073–2087 (2012)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hyun-Chul Kim
    • 1
  • Jong-Hwan Lee
    • 1
  1. 1.Department of Brain Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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