A Gaussian process model of human electrocorticographic data
| Autor: | Patrick M. Daly, Lucy L. W. Owen, Jeremy R. Manning, Katherine W. Scangos, Andrew C. Heusser, Tudor A Muntianu |
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| Rok vydání: | 2017 |
| Předmět: |
electrocorticography (ECoG)
Computer science Cognitive Neuroscience Maximum likelihood Models Neurological Normal Distribution maximum likelihood estimation intracranial electroencephalography (iEEG) Space (mathematics) Correlation Cellular and Molecular Neuroscience Neural activity symbols.namesake Kriging Image Processing Computer-Assisted Humans AcademicSubjects/MED00385 Set (psychology) Gaussian process Structure (mathematical logic) Brain Mapping Likelihood Functions AcademicSubjects/SCI01870 business.industry Brain Pattern recognition local field potential (LFP) symbols epilepsy AcademicSubjects/MED00310 Original Article Electrocorticography Spatiotemporal resolution Artificial intelligence business Gaussian process regression |
| Zdroj: | Cerebral Cortex (New York, NY) |
| Popis: | We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people’s brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual’s brain: given recordings from a limited set of locations in that individual’s brain, along with the observed spatial correlations learned from other people’s recordings, how much can be inferred about ongoing activity at other locations throughout that individual’s brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings. |
| Databáze: | OpenAIRE |
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