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We propose a data fusion method for the fusion of simultaneously acquired functional magnetic resonance imaging (fMRI) and single trial electroencephalography (EEG) data from multiple subjects using multi-set canonical correlation analysis (M-CCA). Our proposed technique utilizes the common time series information in the multimodal datasets to find trial-to-trial covariations across modalities, and...
Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide complementary information about the brain function. We propose a novel scheme to examine associations between these two modalities using canonical correlation analysis (CCA). Our multimodal canonical correlation analysis (mCCA) scheme utilizes inter-subject covariations to quantitatively link the two modalities and...
Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature...
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