| Title: | Estimation of task-related dynamic brain connectivity via data inflation and classification model explainability |
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| Authors: | ID Rogelj, Peter (Author) |
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| Files: |  RAZ_Rogelj_Peter_2025.pdf (1,74 MB)
MD5: 7C4B39DA6003CC49351646803B68D93D
 https://link.springer.com/article/10.1007/s12021-025-09733-6
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FAMNIT - Faculty of Mathematics, Science and Information Technologies
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| Abstract: | Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints of windowing functions, which reduce temporal resolution and hinder explainability of highly dynamic processes. In this work, we propose a novel approach to functional connectivity analysis through the explainability of EEG classification. Unlike conventional methods that condense raw data into extracted features, our approach inflates raw EEG data by decomposition into meaningful components that explain processes in the application domain. To uncover the brain connectivity that affects classification decisions, we introduce a new method of dynamic influence data inflation (DIDI), which extracts signals representing interactions between electrode regions. These inflated data are then classified using an end-to-end neural network classifier architecture designed for raw EEG signals. Saliency map estimation from trained classifiers reveals the connectivity dynamics affecting classification decisions, which can be visualized as dynamic connectivity support maps for improved interpretability. The methodology is demonstrated on two publicly available datasets: one for imagined motor movement classification and the other for emotion classification. The results highlight the dual benefits of our approach: in addition to providing interpretable insights into connectivity dynamics it increases classification accuracy. |
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| Keywords: | EEG, functional connectivity, data inflation, classification, explainability, saliency maps |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 03.06.2025 |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-16 |
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| Numbering: | Vol. 23, article no. ǂ33 |
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| PID: | 20.500.12556/RUP-21320  |
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| UDC: | 004:616.831-073.7-71 |
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| ISSN on article: | 1559-0089 |
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| DOI: | 10.1007/s12021-025-09733-6 |
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| COBISS.SI-ID: | 238192387 |
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| Publication date in RUP: | 04.06.2025 |
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| Views: | 143 |
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| Downloads: | 9 |
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