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Decoding Causal Information Flow in Epileptic Brain Networks Using Granger-Based EEG Connectivity Analysis
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1
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| Subject:
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Brain Mapping Applications in Epilepsy – Across Pediatric and Adult Populations
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Presenter Author:
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| Name:
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Fatemeh Shakibafard
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Author:
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| Name 1:
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Fatemeh Shakibafard - Corresponding - Presenter
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| Affiliation 1:
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PhD Student of Cognitive Neuroscience, Computation and Artificial Intelligence, Institute for Cognitive Science Study, Tehran, Iran
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| Name 2:
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Saeed Setayeshi
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| Affiliation 2:
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Professor of Electrical and Computer Engineering, Amirkabir University of Technolog, Tehran, Iran
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Abstract
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Original Research
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introduction :
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Analyzing brain connectivity provides valuable insights into how neural regions interact and how these interactions change dynamically, especially in neurological disorders such as epilepsy. Understanding the difference between functional and effective connectivity is essential for identifying the directional flow of information across the brain. However, many traditional measures focus only on synchrony or correlation and fail to capture causal influences between regions. This study compares several connectivity approaches to assess their ability to represent directed and causal interactions in EEG data that simulate epileptic seizures.
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Methods:
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Simulated EEG recordings were used to establish a controlled evaluation framework. After standard preprocessing, Independent Component Analysis (ICA) was applied to project the data into the source domain, minimizing the effects of volume conduction. The signals were modeled in the time–frequency domain using multivariate Vector Autoregressive (VAR) models. From these models, five connectivity measures were computed: Directed Transfer Function (DTF), normalized DTF (nDTF), Complex Spectral Density (S), Complex Coherence (Coh), and Granger–Geweke Causality (GGC).
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Results:
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The findings indicate that coherence- and spectral-based metrics (Coh and S) primarily exhibit symmetric connectivity patterns, while DTF and nDTF provide limited directional consistency. In contrast, Granger–Geweke Causality consistently identified asymmetric, time-directed relationships between independent components. Overall, the results demonstrate that Granger-based causal measures offer a more interpretable and reliable representation of directed interactions in epileptic EEG networks. This framework shows promise for future applications in clinical EEG analysis and personalized epilepsy diagnostics.
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Conclusion:
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The study demonstrates that Granger-based causal measures, particularly Granger–Geweke Causality (GGC), can detect the movement of information between brain regions, providing an alternative method for assessing directional and causal interactions compared to standard connectivity metrics. Ultimately, applying this framework to actual clinical EEG recordings may enable researchers to detect unique brain activity patterns, thus contributing to the development of personalized strategies for epilepsy diagnosis and monitoring.
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| Attachment:
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| Keywords:
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EEG Connectivity Epileptic Seizures Directed Connectivity Granger–Geweke Causality (GGC)
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| Key message:
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