Sufficient sampling for kriging prediction of cortical potential in rat, monkey, and human μECoG

Michael Trumpis, Chia Han Chiang, Amy L. Orsborn, Brinnae Bent, Jinghua Li, John A. Rogers, Bijan Pesaran, Gregory Cogan, Jonathan Viventi

Research output: Contribution to journalArticlepeer-review


Objective. Large channel count surface-based electrophysiology arrays (e.g. μECoG) are high-throughput neural interfaces with good chronic stability. Electrode spacing remains ad hoc due to redundancy and nonstationarity of field dynamics. Here, we establish a criterion for electrode spacing based on the expected accuracy of predicting unsampled field potential from sampled sites. Approach. We applied spatial covariance modeling and field prediction techniques based on geospatial kriging to quantify sufficient sampling for thousands of 500 ms μECoG snapshots in human, monkey, and rat. We calculated a probably approximately correct (PAC) spacing based on kriging that would be required to predict μECoG fields at ≤ 10% error for most cases (95% of observations). Main results. Kriging theory accurately explained the competing effects of electrode density and noise on predicting field potential. Across five frequency bands from 4-7 to 75-300 Hz, PAC spacing was sub-millimeter for auditory cortex in anesthetized and awake rats, and posterior superior temporal gyrus in anesthetized human. At 75-300 Hz, sub-millimeter PAC spacing was required in all species and cortical areas. Significance. PAC spacing accounted for the effect of signal-to-noise on prediction quality and was sensitive to the full distribution of non-stationary covariance states. Our results show that μECoG arrays should sample at sub-millimeter resolution for applications in diverse cortical areas and for noise resilience.

Original languageEnglish (US)
Article number036011
JournalJournal of neural engineering
Issue number3
StatePublished - Jun 2021


  • Gaussian processes
  • kriging
  • local field potential
  • micro-electrocorticography
  • spatial filter
  • spatial statistics

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience


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