TY - GEN
T1 - Fusion of concurrent single trial EEG data and FMRI data using multi-set canonical correlation analysis
AU - Correa, Nicolle M.
AU - Eichele, Tom
AU - Adali, Tülay
AU - Li, Yi Ou
AU - Calhoun, Vince D.
PY - 2010/11/8
Y1 - 2010/11/8
N2 - 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 based on these covariations, the data is decomposed into spatial maps for the fMRI data and a corresponding temporal evolution for the EEG data. Additionally, the analysis is performed simultaneously on data from a group of subjects, thus providing an efficient tool to make group inferences about cross-modality covariation. The proposed method is multivariate and hence facilitates the study of brain connectivity along with localization of brain function. We demonstrate the promise of the method in finding covarying trial-to-trial amplitude modulations in an auditory task involving implicit pattern learning.
AB - 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 based on these covariations, the data is decomposed into spatial maps for the fMRI data and a corresponding temporal evolution for the EEG data. Additionally, the analysis is performed simultaneously on data from a group of subjects, thus providing an efficient tool to make group inferences about cross-modality covariation. The proposed method is multivariate and hence facilitates the study of brain connectivity along with localization of brain function. We demonstrate the promise of the method in finding covarying trial-to-trial amplitude modulations in an auditory task involving implicit pattern learning.
KW - Biomedical signal analysis
KW - Electroencephalography
KW - Functional magnetic resonance
KW - Multi-set canonical correlation analysis
KW - Multimodal analysis
UR - http://www.scopus.com/inward/record.url?scp=78049376772&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049376772&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2010.5494919
DO - 10.1109/ICASSP.2010.5494919
M3 - Conference contribution
AN - SCOPUS:78049376772
SN - 9781424442966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5438
EP - 5441
BT - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
T2 - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Y2 - 14 March 2010 through 19 March 2010
ER -