TY - GEN
T1 - Extended Electrophysiological Source Imaging with Spatial Graph Filters
AU - Liu, Feng
AU - Wan, Guihong
AU - Semenov, Yevgeniy R.
AU - Purdon, Patrick L.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - EEG/MEG source imaging
KW - Graph signal processing
KW - Low-rank representation
KW - Spatial graph filter
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U2 - 10.1007/978-3-031-16431-6_10
DO - 10.1007/978-3-031-16431-6_10
M3 - Conference contribution
AN - SCOPUS:85138830977
SN - 9783031164309
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 99
EP - 109
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
A2 - Wang, Linwei
A2 - Dou, Qi
A2 - Fletcher, P. Thomas
A2 - Speidel, Stefanie
A2 - Li, Shuo
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -