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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/17/11/517 |
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| Summary: | 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. |
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| ISSN: | 1999-4893 |