Semantic attributes are encoded in human electrocorticographic signals during visual object recognition

Kyle Rupp, Matthew Roos, Griffin Milsap, Carlos Caceres, Christopher Ratto, Mark Chevillet, Nathan E. Crone, Michael Wolmetz

Research output: Contribution to journalArticlepeer-review

Abstract

Non-invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non-invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high-dimensional encoding models to map semantic attributes to spectral-temporal features of the task-related neural responses. Using these semantic attribute encoding models, untrained objects were decoded with accuracies comparable to whole-brain functional Magnetic Resonance Imaging (fMRI), and we observed that high-gamma activity (70–110 Hz) at basal occipitotemporal electrodes was associated with specific semantic dimensions (manmade-animate, canonically large-small, and places-tools). Individual patient results were in close agreement with reports from other imaging modalities on the time course and functional organization of semantic processing along the ventral visual pathway during object recognition. The semantic attribute encoding model approach is critical for decoding objects absent from a training set, as well as for studying complex semantic encodings without artificially restricting stimuli to a small number of semantic categories.

Original languageEnglish (US)
Pages (from-to)318-329
Number of pages12
JournalNeuroImage
Volume148
DOIs
StatePublished - Mar 1 2017

Keywords

  • Electrocorticography
  • Encoding models
  • High-gamma activity
  • Object recognition
  • Semantics

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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