A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation
This study presents a method for generating synthetic electroencephalography (<i>EEG)</i> 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 trut...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/517 |
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| author | Zoran Šverko Saša Vlahinić Peter Rogelj |
| author_facet | Zoran Šverko Saša Vlahinić Peter Rogelj |
| author_sort | Zoran Šverko |
| collection | DOAJ |
| description | This study presents a method for generating synthetic electroencephalography (<i>EEG)</i> 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 <i>EEG</i> signals. To address this, we propose a framework for generating synthetic <i>EEG</i> signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (<i>GC</i>). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for <i>GC</i> analysis. The findings could guide the development of more accurate dynamic connectivity techniques. |
| format | Article |
| id | doaj-art-4d5bdb4ad4324a4e8e4f8334a183cfe8 |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-4d5bdb4ad4324a4e8e4f8334a183cfe82025-08-20T02:08:07ZengMDPI AGAlgorithms1999-48932024-11-01171151710.3390/a17110517A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal GenerationZoran Šverko0Saša Vlahinić1Peter Rogelj2Department of Electric Power Systems, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaDepartment of Automation and Electronics, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, CroatiaFaculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 6000 Koper, SloveniaThis study presents a method for generating synthetic electroencephalography (<i>EEG)</i> 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 <i>EEG</i> signals. To address this, we propose a framework for generating synthetic <i>EEG</i> signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (<i>GC</i>). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for <i>GC</i> analysis. The findings could guide the development of more accurate dynamic connectivity techniques.https://www.mdpi.com/1999-4893/17/11/517electroencephalographygranger causalitysynthetic signalsdynamic connectivity |
| spellingShingle | Zoran Šverko Saša Vlahinić Peter Rogelj A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation Algorithms electroencephalography granger causality synthetic signals dynamic connectivity |
| title | A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation |
| title_full | A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation |
| title_fullStr | A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation |
| title_full_unstemmed | A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation |
| title_short | A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation |
| title_sort | framework for evaluating dynamic directed brain connectivity estimation methods using synthetic eeg signal generation |
| topic | electroencephalography granger causality synthetic signals dynamic connectivity |
| url | https://www.mdpi.com/1999-4893/17/11/517 |
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