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Multiscale Causal Connectivity Analysis by Canonical Correlation: Theory and Application to Epileptic Brain
- Source :
- IEEE Transactions on Biomedical Engineering. 58:3088-3096
- Publication Year :
- 2011
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2011.
-
Abstract
- Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period.
- Subjects :
- Multivariate statistics
Biomedical Engineering
Machine learning
computer.software_genre
Data modeling
Young Adult
Granger causality
Humans
Computer Simulation
Autoregressive–moving-average model
Time series
Mathematics
Brain Mapping
Epilepsy
business.industry
Brain
Electroencephalography
Signal Processing, Computer-Assisted
Pattern recognition
Magnetic Resonance Imaging
Causality
Autoregressive model
Multivariate Analysis
Female
Artificial intelligence
Tomography, X-Ray Computed
Canonical correlation
business
computer
Algorithms
Subjects
Details
- ISSN :
- 15582531 and 00189294
- Volume :
- 58
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Biomedical Engineering
- Accession number :
- edsair.doi.dedup.....e2bddc92c37f1cf959fc48df5cbb235c