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A Gaussian Process Model of Human Electrocorticographic Data.
Cerebral Cortex ( IF 3.7 ) Pub Date : 2020-06-04 , DOI: 10.1093/cercor/bhaa115
Lucy L W Owen 1 , Tudor A Muntianu 1 , Andrew C Heusser 1, 2 , Patrick M Daly 3 , Katherine W Scangos 3 , Jeremy R Manning 1
Affiliation  

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.

中文翻译:

人类皮层电数据的高斯过程模型。

我们提出了一种基于模型的方法,用于使用标准的人类颅内记录推断毫米级空间分辨率和毫秒级时间分辨率的全脑神经活动。我们的方法做出了简化假设,即不同人的大脑表现出相似的相关结构,并且活动和相关模式在空间上平滑变化。然后可以问,对于任意个体的大脑:给定来自该个体大脑中一组有限位置的记录,以及从其他人的记录中观察到的空间相关性,可以推断出该个体整个大脑中其他位置正在进行的活动有多少脑?我们表明我们的方法可以泛化到人和任务中,
更新日期:2020-06-04
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