Recordings of epidural field potentials (EFPs) allow to acquire neuronal activity over a large region of cortical tissue with minimal invasiveness. Because electrodes are placed on top of the dura and do not enter the neuronal tissue, EFPs offer intriguing options for both clinical and basic science research. On the other hand, EFPs represent the integrated activity of larger neuronal populations, possess a higher trial-by-trial variability, and a reduced signal-to-noise ratio due the additional barrier of the dura. It is thus unclear whether and to what extent EFPs have sufficient spatial selectivity to allow for conclusions about the underlying functional cortical architecture, and whether single EFP trials provide enough information on the short time scales relevant for many clinical and basic neuroscience purposes. We here use the high spatial resolution of primary visual cortex to address these issues and investigate the extent to which very short EFP traces allow reliable decoding of spatial information. We briefly presented different visual objects at one out of nine closely adjacent locations and recorded neuronal activity with a high-density, epidural multi-electrode array in three macaque monkeys. Using receiver-operating characteristics to identify most-informative data, machine-learning algorithms provided close-to-perfect classification rates for all 27 stimulus conditions. A binary classifier applying a simple max function on ROC-selected data further showed that single trials might be classified with 100% performance even without advanced offline classifiers. Thus, although highly variable, EFPs constitute an extremely valuable source of information and offer new perspectives for minimally invasive recording of large-scale networks.
Journal of Neurophysiology – The American Physiological Society
Published: Aug 8, 2019
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