Robustness of brain state identification in synthetic phase-coupled neurodynamics using Hidden Markov Models
Hidden Markov Models (HMMs) have emerged as a powerful tool for analyzing time series of neural activity. Gaussian HMMs and their time-resolved extension, Time-Delay Embedded HMMs (TDE-HMMs), have been instrumental in detecting discrete brain states in the form of temporal sequences of large-scale b...
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| Автори: | , , , , , , , , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Frontiers Media S.A.
2025-04-01
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| Серія: | Frontiers in Systems Neuroscience |
| Предмети: | |
| Онлайн доступ: | https://www.frontiersin.org/articles/10.3389/fnsys.2025.1548437/full |
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