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Independent Component Analysis of EEG-fMRI data for studying epilepsy and epileptic seizures
- Source :
- EMBC
- Publication Year :
- 2013
- Publisher :
- IEEE, 2013.
-
Abstract
- Here we present a method for classifying fMRI independent components (ICs) by using an optimized algorithm for the individuation of noisy signals from sources of interest. The method was applied to estimate brain activations from combined EEG-fMRI data for the exploration of epilepsy. Spatial ICA was performed using the above-mentioned optimized algorithm and other three popular algorithms. ICs were sorted considering the value: of the coefficients of determination R2, obtained from the multiple regression analysis with morphometric maps of cerebral matter; of the kurtosis, which features the signal energy. The validation of the method was performed comparing the brain activations obtained with those resulted using the General Linear Model (GLM). The ICA-derived activations in different datasets comprised subareas of the GLM-revealed activations, even if the volume and the shape of activated areas do not correspond exactly. The method proposed also detects additional negative regions implicated in a default mode of brain activity, and not clearly identified by GLM. Compared with a traditional GLM approach, the ICA one provides a flexible way to analyze fMRI data that reduces the assumptions placed upon the hemodynamic response of the brain and the temporal constrains.
- Subjects :
- Male
Computer science
Speech recognition
Electroencephalography
EEG-fMRI
Epilepsy
Seizures
medicine
Humans
Child
Default mode network
General linear model
Brain Mapping
medicine.diagnostic_test
business.industry
Brain
Signal Processing, Computer-Assisted
Regression analysis
Pattern recognition
medicine.disease
Magnetic Resonance Imaging
Independent component analysis
Child, Preschool
Linear Models
Kurtosis
Female
Artificial intelligence
Artifacts
business
Algorithms
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Accession number :
- edsair.doi.dedup.....13d6505a8bd39154b2d5172e9a0f5dbc