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Temporal Autocorrelation-Based Beamforming With MEG Neuroimaging Data

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  • Jian Zhang
  • Li Su

Abstract

Characterizing the brain source activity using magnetoencephalography (MEG) requires solving an ill-posed inverse problem. Most source reconstruction procedures are performed in terms of power comparison. However, in the presence of voxel-specific noises, the direct power analysis can be misleading due to the power distortion as suggested by our multiple trial MEG study on a face-perception experiment. To tackle the issue, we propose a temporal autocorrelation-based method for the above analysis. The new method improves the face-perception analysis and identifies several differences between neuronal responses to face and scrambled-face stimuli. By the simulated and real data analyses, we demonstrate that compared to the existing methods, the new proposal can be more robust to voxel-specific noises without compromising on its accuracy in source localization. We further establish the consistency for estimating the proposed index when the number of sensors and the number of time instants are sufficiently large. In particular, we show that the proposed procedure can make a better focus on true sources than its precedents in terms of peak segregation coefficient. Supplementary materials for this article are available online.

Suggested Citation

  • Jian Zhang & Li Su, 2015. "Temporal Autocorrelation-Based Beamforming With MEG Neuroimaging Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1375-1388, December.
  • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:512:p:1375-1388
    DOI: 10.1080/01621459.2015.1054488
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    File URL: http://hdl.handle.net/10.1080/01621459.2015.1054488
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    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
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