TY - GEN
T1 - Fusing Simultaneous EEG and fMRI Using Functional and Anatomical Information
AU - Hansen, Sofie Therese
AU - Winkler, Irene
AU - Hansen, Lars Kai
AU - Muller, Klaus Robert
AU - Dahne, Sven
N1 - Funding Information:
LKH acknowledges support from the Danish Lundbeck Foundation via the Center for Integrated Molecular Brain Imaging (CIMBI). SD acknowledges funding by the German Research Foundation (DFG) grant no. MU 987/19-1 and support by the Bernstein Center for Computational Neuroscience, Berlin through the graduate program GRK 1589/1. KRM acknowledges support by the Brain Korea 21 Plus Program through the National Research Foundation of Korea funded by the Ministry of Education.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/16
Y1 - 2015/9/16
N2 - Simultaneously measuring electro physical and hemodynamic signals has become more accessible in the last years and the need for modeling techniques that can fuse the modalities is growing. In this work we augment a specific fusion method, the multimodal Source Power Co-modulation (mSPoC), to not only use functional but also anatomical information. The goal is to extract correlated source components from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Anatomical information enters our proposed extension to mSPoC via the forward model, which relates the activity on cortex level to the EEG sensors. The augmented mSPoC is shown to outperform the original version in realistic simulations where the signal to noise ratio is low or where training epochs are scarce.
AB - Simultaneously measuring electro physical and hemodynamic signals has become more accessible in the last years and the need for modeling techniques that can fuse the modalities is growing. In this work we augment a specific fusion method, the multimodal Source Power Co-modulation (mSPoC), to not only use functional but also anatomical information. The goal is to extract correlated source components from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Anatomical information enters our proposed extension to mSPoC via the forward model, which relates the activity on cortex level to the EEG sensors. The augmented mSPoC is shown to outperform the original version in realistic simulations where the signal to noise ratio is low or where training epochs are scarce.
KW - EEG
KW - Fusion
KW - Multimodal neuroimaging
KW - Oscillation
KW - fMRI
UR - http://www.scopus.com/inward/record.url?scp=84961845504&partnerID=8YFLogxK
U2 - 10.1109/PRNI.2015.22
DO - 10.1109/PRNI.2015.22
M3 - Conference contribution
AN - SCOPUS:84961845504
T3 - Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
SP - 33
EP - 36
BT - Proceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
Y2 - 10 June 2015 through 12 June 2015
ER -