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Open AccessArticle

A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks

1
Department of Biomedical Engineering, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, Ukraine
2
Data & Analytics, Ciklum, London WC1 A 2TH, UK
3
Department of Engineering, University of Palermo, 90133 Palermo, Italy
4
Department of Electronic Engineering, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 03056 Kyiv, Ukraine
5
Clinical Hospital “Psychiatry”, 03056 Kyiv, Ukraine
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(9), 657; https://doi.org/10.3390/brainsci10090657
Received: 27 August 2020 / Revised: 13 September 2020 / Accepted: 15 September 2020 / Published: 22 September 2020
(This article belongs to the Special Issue Human Brain Dynamics: Latest Advances and Prospects)
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; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy. View Full-Text
Keywords: epilepsy; information theory; EEG; information storage; information transfer; vector autoregressive modeling; common spatial patterns; independent component analysis epilepsy; information theory; EEG; information storage; information transfer; vector autoregressive modeling; common spatial patterns; independent component analysis
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Kotiuchyi, I.; Pernice, R.; Popov, A.; Faes, L.; Kharytonov, V. A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks. Brain Sci. 2020, 10, 657.

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