Robust State-space Reconstruction of Brain Dynamics via Bootstrap Monte Carlo SSA
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915527349239808 |
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| author | Wiafe, Sir-Lord Hinsley, Carter Calhoun, Vince D. |
| author_facet | Wiafe, Sir-Lord Hinsley, Carter Calhoun, Vince D. |
| contents | Reconstructing latent state-space geometry from time series provides a powerful route to studying nonlinear dynamics across complex systems. Delay-coordinate embedding provides the theoretical basis but assumes long, noise-free recordings, which many domains violate. In neuroimaging, for example, fMRI is short and noisy; low sampling and strong red noise obscure oscillations and destabilize embeddings. We propose bootstrap Monte Carlo SSA with a red-noise null and bootstrap stability to retain only oscillatory modes that reproducibly exceed noise. This produces reconstructions that are red-noise-robust and mode-robust, enhancing determinism and stabilizing subsequent embeddings. Our results show that BMC-SSA improves the reliability of functional measures and uncovers differences in state-space dynamics in fMRI, offering a general framework for robust embeddings of noisy, finite signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust State-space Reconstruction of Brain Dynamics via Bootstrap Monte Carlo SSA Wiafe, Sir-Lord Hinsley, Carter Calhoun, Vince D. Neurons and Cognition Reconstructing latent state-space geometry from time series provides a powerful route to studying nonlinear dynamics across complex systems. Delay-coordinate embedding provides the theoretical basis but assumes long, noise-free recordings, which many domains violate. In neuroimaging, for example, fMRI is short and noisy; low sampling and strong red noise obscure oscillations and destabilize embeddings. We propose bootstrap Monte Carlo SSA with a red-noise null and bootstrap stability to retain only oscillatory modes that reproducibly exceed noise. This produces reconstructions that are red-noise-robust and mode-robust, enhancing determinism and stabilizing subsequent embeddings. Our results show that BMC-SSA improves the reliability of functional measures and uncovers differences in state-space dynamics in fMRI, offering a general framework for robust embeddings of noisy, finite signals. |
| title | Robust State-space Reconstruction of Brain Dynamics via Bootstrap Monte Carlo SSA |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2510.00011 |