A Gaussian process model of human electrocorticographic data

Abstract

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 peoples brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individuals brain: given recordings from a limited set of locations in that individuals brain, along with the observed spatial correlations learned from other peoples recordings, how much can be inferred about ongoing activity at other locations throughout that individuals 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.