TY - GEN
T1 - Supervised discriminative EEG brain source imaging with graph regularization
AU - Liu, Feng
AU - Hosseini, Rahilsadat
AU - Rosenberger, Jay
AU - Wang, Shouyi
AU - Su, Jianzhong
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - As Electroencephalography (EEG) is a non-invasive brain imaging technique that records the electric field on the scalp instead of direct measuring activities of brain voxels on the cortex, many approaches were proposed to estimate the activated sources due to its significance in neuroscience research and clinical applications. However, since most part of the brain activity is composed of the spontaneous neural activities or non-task related activations, true task relevant activation sources can be very challenging to be discovered given strong background signals. For decades, the EEG source imaging problem was solved in an unsupervised way without taking into consideration the label information that representing different brain states (e.g. happiness, sadness, and surprise). A novel model for solving EEG inverse problem called Graph Regularized Discriminative Source Imaging (GRDSI) was proposed, which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model is capable of estimating the discriminative brain sources under different brain states and encouraging intra-class consistency. Simulation results show the effectiveness of our proposed framework in retrieving the discriminative sources.
AB - As Electroencephalography (EEG) is a non-invasive brain imaging technique that records the electric field on the scalp instead of direct measuring activities of brain voxels on the cortex, many approaches were proposed to estimate the activated sources due to its significance in neuroscience research and clinical applications. However, since most part of the brain activity is composed of the spontaneous neural activities or non-task related activations, true task relevant activation sources can be very challenging to be discovered given strong background signals. For decades, the EEG source imaging problem was solved in an unsupervised way without taking into consideration the label information that representing different brain states (e.g. happiness, sadness, and surprise). A novel model for solving EEG inverse problem called Graph Regularized Discriminative Source Imaging (GRDSI) was proposed, which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model is capable of estimating the discriminative brain sources under different brain states and encouraging intra-class consistency. Simulation results show the effectiveness of our proposed framework in retrieving the discriminative sources.
KW - EEG source imaging
KW - Graph regularization
KW - Inverse problem
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85029371777&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-66182-7_57
DO - 10.1007/978-3-319-66182-7_57
M3 - Conference contribution
AN - SCOPUS:85029371777
SN - 9783319661810
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 495
EP - 504
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Descoteaux, Maxime
A2 - Duchesne, Simon
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Maier-Hein, Lena
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -