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RESEARCH PRODUCT
A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks
Volodymyr KharytonovIvan KotiuchyiLuca FaesAnton PopovRiccardo Pernicesubject
Information transfercommon spatial patternComputer science0206 medical engineeringcommon spatial patterns02 engineering and technologyElectroencephalographyInformation theoryArticlelcsh:RC321-57103 medical and health sciencesEpilepsy0302 clinical medicineinformation storagemedicineinformation transferIctalEEGGeneralized epilepsylcsh:Neurosciences. Biological psychiatry. Neuropsychiatryinformation theorymedicine.diagnostic_testbusiness.industryGeneral NeurosciencePattern recognitionmedicine.disease020601 biomedical engineeringIndependent component analysismedicine.anatomical_structurevector autoregressive modelingindependent component analysisScalpSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaepilepsyArtificial intelligencebusiness030217 neurology & neurosurgerydescription
This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-09-22 | Brain Sciences |