Toward A database of intracranial electrophysiology during natural language presentation
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Author(s) • • • • • • • •
Kaestner, Erik
Morgan, Adam Milton
Snider, Joseph
Zhan, Meilin
Jiang, Xi
Levy, Roger
Ferreira, Victor S.
Thesen, Thomas
Halgren, Eric
Date Issued
July 2018
Journal
Language, Cognition and Neuroscience
Publisher
Informa UK Limited
Citation
Kaestner, Erik et al. "Toward A database of intracranial electrophysiology during natural language presentation." Language, Cognition and Neuroscience (July 2018) © 2018 Informa UK Limited
Version
Author's final manuscript
Abstract
Intracranial electrophysiology (iEEG) studies using cognitive tasks contribute to the understanding of the neural basis of language. However, though iEEG is recorded continuously during clinical treatment, due to patient considerations task time is limited. To increase the usefulness of iEEG recordings for language study we provided patients with a tablet pre-loaded with media filled with natural language, wirelessly synchronised to clinical iEEG. This iEEG data collected and time-locked to natural language presentation is particularly applicable for studying the neural basis of combining words into larger contexts. We validate this approach with pilot analyses involving words heard during a movie, tagging syntactic properties and verb contextual probabilities. Event-related averages of high-frequency power (70–170 Hz) identified bilateral perisylvian electrodes with differential responses to syntactic class and a linear regression identified activity associated with contextual probabilities, demonstrating the usefulness of aligning media to iEEG. We imagine future multi-site collaborations building an “intracranial neurolinguistic corpus”. Keywords: Intracranial electrophysiology; natural language; contextual probability
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1080/23273798.2018.1500262