TY - GEN
T1 - EEG source imaging based on spatial and temporal graph structures
AU - Qin, Jing
AU - Liu, Feng
AU - Wang, Shouyi
AU - Rosenberger, Jay
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - EEG serves as an essential tool for brain source localization due to its high temporal resolution. However, the inference of brain activities from the EEG data is, in general, a challenging ill-posed inverse problem. To better retrieve task related discriminative source patches from strong spontaneous background signals, we propose a novel EEG source imaging model based on spatial and temporal graph structures. In particular, graph fractional-order total variation (gFOTV) is used to enhance spatial smoothness, and the label information of brain state is enclosed in a temporal graph regularization term to guarantee intra-class consistency of estimated sources. The proposed model is efficiently solved by the alternating direction method of multipliers (ADMM). A two-stage algorithm is proposed as well to further improve the result. Numerical experiments have shown that our method localizes source extents more effectively than the benchmark methods.
AB - EEG serves as an essential tool for brain source localization due to its high temporal resolution. However, the inference of brain activities from the EEG data is, in general, a challenging ill-posed inverse problem. To better retrieve task related discriminative source patches from strong spontaneous background signals, we propose a novel EEG source imaging model based on spatial and temporal graph structures. In particular, graph fractional-order total variation (gFOTV) is used to enhance spatial smoothness, and the label information of brain state is enclosed in a temporal graph regularization term to guarantee intra-class consistency of estimated sources. The proposed model is efficiently solved by the alternating direction method of multipliers (ADMM). A two-stage algorithm is proposed as well to further improve the result. Numerical experiments have shown that our method localizes source extents more effectively than the benchmark methods.
KW - Alternating Direction Method of Multiplier (ADMM)
KW - EEG Source Imaging
KW - Graph Fractional-Order Total Variation
KW - Graph Regularization
UR - http://www.scopus.com/inward/record.url?scp=85050678142&partnerID=8YFLogxK
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U2 - 10.1109/IPTA.2017.8310089
DO - 10.1109/IPTA.2017.8310089
M3 - Conference contribution
AN - SCOPUS:85050678142
T3 - Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
SP - 1
EP - 6
BT - Proceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
T2 - 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Y2 - 28 November 2017 through 1 December 2017
ER -