Objective: Adaptive beamformer methods that have been extensively used for functional brain imaging using EEG/MEG (magnetoencephalography) signals are sensitive to model mismatches. We propose a robust minimum variance beamformer (RMVB) technique, which explicitly incorporates the uncertainty of the lead field matrix into the estimation of spatial-filter weights that are subsequently used to perform the imaging. Methods: The uncertainty of the lead field is modeled by ellipsoids in the RMVB method; these hyperellipsoids (ellipsoids in higher dimensions) define regions of uncertainty for a given nominal lead field vector. These ellipsoids are estimated empirically by sampling lead field vectors surrounding each point of the source space, or more generally by building several forward models for the source space. Once these uncertainty regions (ellipsoids) are estimated, they are used to perform the source-imaging task. Computer simulations are conducted to evaluate the performance of the proposed RMVB technique. Results: Our results show that robust beamformers can outperform conventional beamformers in terms of localization error, recovering source dynamics, and estimation of the underlying source extents when uncertainty in the lead field matrix is properly determined and modeled. Conclusion: The RMVB can be substituted for conventional beamformers, especially in applications where source imaging is performed off-line, and computational speed and complexity are not of major concern. Significance: A high-quality source imaging can be utilized in various applications, such as determining the epileptogenic zone in medically intractable epilepsy patients or estimating the time course of activity, which is a required step for computing the functional connectivity of brain networks.
|Original language||English (US)|
|Number of pages||10|
|Journal||IEEE Transactions on Biomedical Engineering|
|State||Published - Oct 2018|
Bibliographical noteFunding Information:
Manuscript received April 15, 2018; revised June 27, 2018; accepted July 16, 2018. Date of publication July 24, 2018; date of current version September 18, 2018. This work was supported in part by NIH under Grants EB021027, NS096761, MH114233, and AT009263, and NSF CAREER CCF-1651825. (Corresponding author: Bin He.) S. A. H. Hosseini, A. Sohrabpour, and M. Akc¸akaya are with the Department of Biomedical Engineering, University of Minnesota.
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- Adaptive beamformer
- electromagnetic source imaging
- inverse problem
- linearly constrained minimum variance beamformer
- robust beamformer
- robust minimum variance beamformer