Abstract
Electrophysiological Source Imaging (ESI) refers to the process of localizing the brain source activation patterns given measured Electroencephalography (EEG) or Magnetoencephalography (MEG) signal from the scalp. Recent studies have focused on designing sophisticated neurophysiologically plausible regularizations or efficient estimation frameworks to solve the ESI problem, with the underlying assumption that brain source activation has some specific structures. Estimation of both source location and its extents is important in clinical applications. However, estimating the high dimensional extended location is challenging due to the highly coherent columns in the leadfield matrix, resulting in a reconstructed spiky spurious sources. In this work, we describe an efficient and accurate framework by exploiting the graph structure defined in the 3D mesh of the brain. Specifically, we decompose the graph signal representation in the source space into low-, medium-, and high-frequency subspaces, and project the source signal into the graph low-frequency subspace. We further introduce a low-rank representation with temporal graph regularization in the projected space to build the ESI framework, which can be efficiently solved. Experiments with simulated data and real world EEG data demonstrated the superiority of the proposed paradigm for estimating brain source extents.
F. Liu and G. Wan—Contributed equally to this work.
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Liu, F., Wan, G., Semenov, Y.R., Purdon, P.L. (2022). Extended Electrophysiological Source Imaging with Spatial Graph Filters. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_10
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