TY - GEN
T1 - Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality
AU - Wan, Guihong
AU - Jiao, Meng
AU - Ju, Xinglong
AU - Zhang, Yu
AU - Schweitzer, Haim
AU - Liu, Feng
N1 - Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2023/6/27
Y1 - 2023/6/27
N2 - Electrophysiological Source Imaging (ESI) refers to reconstructing the underlying brain source activation from noninvasive Electroencephalography (EEG) and Magnetoencephalography (MEG) measurements on the scalp. Estimating the source locations and their extents is a fundamental tool in clinical and neuroscience applications. However, the estimation is challenging because of the ill-posedness and high coherence in the leadfield matrix as well as the noise in the EEG/MEG data. In this work, we proposed a combinatorial search framework to address the ESI problem with a provable optimality guarantee. Specifically, by exploiting the graph neighborhood information in the brain source space, we converted the ESI problem into a graph search problem and designed a combinatorial search algorithm under the framework of A∗ to solve it. The proposed algorithm is guaranteed to give an optimal solution to the ESI problem. Experimental results on both synthetic data and real epilepsy EEG data demonstrated that the proposed algorithm could faithfully reconstruct the source activation in the brain.
AB - Electrophysiological Source Imaging (ESI) refers to reconstructing the underlying brain source activation from noninvasive Electroencephalography (EEG) and Magnetoencephalography (MEG) measurements on the scalp. Estimating the source locations and their extents is a fundamental tool in clinical and neuroscience applications. However, the estimation is challenging because of the ill-posedness and high coherence in the leadfield matrix as well as the noise in the EEG/MEG data. In this work, we proposed a combinatorial search framework to address the ESI problem with a provable optimality guarantee. Specifically, by exploiting the graph neighborhood information in the brain source space, we converted the ESI problem into a graph search problem and designed a combinatorial search algorithm under the framework of A∗ to solve it. The proposed algorithm is guaranteed to give an optimal solution to the ESI problem. Experimental results on both synthetic data and real epilepsy EEG data demonstrated that the proposed algorithm could faithfully reconstruct the source activation in the brain.
UR - http://www.scopus.com/inward/record.url?scp=85168251318&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85168251318&partnerID=8YFLogxK
U2 - 10.1609/aaai.v37i10.2636126471
DO - 10.1609/aaai.v37i10.2636126471
M3 - Conference contribution
AN - SCOPUS:85168251318
T3 - Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
SP - 12491
EP - 12499
BT - AAAI-23 Technical Tracks 10
A2 - Williams, Brian
A2 - Chen, Yiling
A2 - Neville, Jennifer
T2 - 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Y2 - 7 February 2023 through 14 February 2023
ER -