TY - GEN
T1 - Estimating latent brain sources with low-rank representation and graph regularization
AU - Liu, Feng
AU - Wang, Shouyi
AU - Qin, Jing
AU - Lou, Yifei
AU - Rosenberger, Jay
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - To infer latent brain source activation patterns under different cognitive tasks is an integral step to understand how our brain works. Traditional electroencephalogram (EEG) Source Imaging (ESI) methods usually do not distinguish task-related and spurious non-task-related sources that jointly generate EEG signals, which inevitably yield misleading reconstructed activation patterns. In this research, we assume that the task-related source signal intrinsically has a low-rank property, which is exploited to infer the true task-related EEG sources location. Although the true task-related source signal is sparse and low-rank, the contribution of spurious sources scattering over the source space with intermittent activation patterns makes the actual source space lose the low-rank property. To reconstruct a low-rank true source, we propose a novel ESI model that involves a spatial low-rank representation and a temporal Laplacian graph regularization, the latter of which guarantees the temporal smoothness of the source signal and eliminate the spurious ones. To solve the proposed model, an augmented Lagrangian objective function is formulated and an algorithm in the framework of alternating direction method of multipliers (ADMM) is proposed. Numerical results illustrate the effectivenesks of the proposed method in terms of reconstruction accuracy with high efficiency.
AB - To infer latent brain source activation patterns under different cognitive tasks is an integral step to understand how our brain works. Traditional electroencephalogram (EEG) Source Imaging (ESI) methods usually do not distinguish task-related and spurious non-task-related sources that jointly generate EEG signals, which inevitably yield misleading reconstructed activation patterns. In this research, we assume that the task-related source signal intrinsically has a low-rank property, which is exploited to infer the true task-related EEG sources location. Although the true task-related source signal is sparse and low-rank, the contribution of spurious sources scattering over the source space with intermittent activation patterns makes the actual source space lose the low-rank property. To reconstruct a low-rank true source, we propose a novel ESI model that involves a spatial low-rank representation and a temporal Laplacian graph regularization, the latter of which guarantees the temporal smoothness of the source signal and eliminate the spurious ones. To solve the proposed model, an augmented Lagrangian objective function is formulated and an algorithm in the framework of alternating direction method of multipliers (ADMM) is proposed. Numerical results illustrate the effectivenesks of the proposed method in terms of reconstruction accuracy with high efficiency.
KW - Alternating direction method of multiplier (ADMM)
KW - EEG source imaging
KW - Graph regularization
KW - Low rank representation
UR - http://www.scopus.com/inward/record.url?scp=85058577779&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058577779&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05587-5_29
DO - 10.1007/978-3-030-05587-5_29
M3 - Conference contribution
AN - SCOPUS:85058577779
SN - 9783030055868
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 304
EP - 316
BT - Brain Informatics - International Conference, BI 2018, Proceedings
A2 - Yang, Yang
A2 - Yamamoto, Vicky
A2 - Wang, Shouyi
A2 - Jones, Erick
A2 - Su, Jianzhong
A2 - Mitchell, Tom
A2 - Iasemidis, Leon
T2 - International Conference on Brain Informatics, BI 2018
Y2 - 7 December 2018 through 9 December 2018
ER -