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Algorithms
Volume 17
Issue 11
10.3390/a17110517
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Article

A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation †

by
Zoran Šverko
1,*,
Saša Vlahinić
2 and
Peter Rogelj
3
1
Department of Electric Power Systems, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
2
Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
3
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia
*
Author to whom correspondence should be addressed.
†
This article is a revised and expanded version of a paper entitled Generation of Synthetic EEG Signals for Testing Dynamic Brain Connectivity Estimation Methods, which was presented at 6th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2024), Funchal, Portugal, 17–19 April 2024.
Algorithms 2024, 17(11), 517; https://doi.org/10.3390/a17110517
Submission received: 6 September 2024 / Revised: 5 November 2024 / Accepted: 7 November 2024 / Published: 9 November 2024
(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)
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Abstract

This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To address this, we propose a framework for generating synthetic EEG signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (GC). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for GC analysis. The findings could guide the development of more accurate dynamic connectivity techniques.
Keywords: electroencephalography; granger causality; synthetic signals; dynamic connectivity electroencephalography; granger causality; synthetic signals; dynamic connectivity

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MDPI and ACS Style

Šverko, Z.; Vlahinić, S.; Rogelj, P. A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation. Algorithms 2024, 17, 517. https://doi.org/10.3390/a17110517

AMA Style

Šverko Z, Vlahinić S, Rogelj P. A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation. Algorithms. 2024; 17(11):517. https://doi.org/10.3390/a17110517

Chicago/Turabian Style

Šverko, Zoran, Saša Vlahinić, and Peter Rogelj. 2024. "A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation" Algorithms 17, no. 11: 517. https://doi.org/10.3390/a17110517

APA Style

Šverko, Z., Vlahinić, S., & Rogelj, P. (2024). A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation. Algorithms, 17(11), 517. https://doi.org/10.3390/a17110517

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