TY - GEN
T1 - Rejuvenating Classical Source Localization Methods with Spatial Graph Filters
AU - Yang, Shihao
AU - Jiao, Meng
AU - Xiang, Jing
AU - Kalkanis, Daphne
AU - Sun, Hai
AU - Liu, Feng
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support the clinical decision making, it is important to estimate not only the exact location of source signal but also the boundary of extended source activation. Traditional methods usually render over-diffuse or sparse solution, which limits the source extent estimation accuracy. In this work, we exploit the graph structure defined in the 3D mesh of the brain by decomposing the spatial graph signal into low-, medium-, and high-frequency sub-spaces, and leverage the low frequency components of graph Fourier basis to approximate the extended region of source activation. We integrate the classical source localization methods with the low frequency subspace components derived from the spatial graph signal. The proposed method can effectively reconstruct focal extent patterns and significantly improve the performance compared to classical algorithms through both synthetic data and real EEG data.
AB - EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support the clinical decision making, it is important to estimate not only the exact location of source signal but also the boundary of extended source activation. Traditional methods usually render over-diffuse or sparse solution, which limits the source extent estimation accuracy. In this work, we exploit the graph structure defined in the 3D mesh of the brain by decomposing the spatial graph signal into low-, medium-, and high-frequency sub-spaces, and leverage the low frequency components of graph Fourier basis to approximate the extended region of source activation. We integrate the classical source localization methods with the low frequency subspace components derived from the spatial graph signal. The proposed method can effectively reconstruct focal extent patterns and significantly improve the performance compared to classical algorithms through both synthetic data and real EEG data.
KW - EEG/MEG Source Imaging
KW - Graph Signal Processing
KW - Inverse Problem
KW - Spatial Graph Filter
UR - http://www.scopus.com/inward/record.url?scp=85172417504&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85172417504&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43075-6_25
DO - 10.1007/978-3-031-43075-6_25
M3 - Conference contribution
AN - SCOPUS:85172417504
SN - 9783031430749
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 286
EP - 296
BT - Brain Informatics - 16th International Conference, BI 2023, Proceedings
A2 - Liu, Feng
A2 - Wang, Hongjun
A2 - Zhang, Yu
A2 - Kuai, Hongzhi
A2 - Stephen, Emily P.
T2 - 16th International Conference on Brain Informatics, BI 2023
Y2 - 1 August 2023 through 3 August 2023
ER -