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<p>Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain’s linguistic capabilities. ECoG offers high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas. Here, we share a dataset of nine ECoG participants with 1,330 electrodes listening to a 30-minute audio podcast. The richness of this naturalistic stimulus can be used for various research questions, from auditory perception to narrative integration. In addition to the neural data, we extracted linguistic features of the stimulus ranging from phonetic information to large language model word embeddings. We use these linguistic features in encoding models that relate stimulus properties to neural activity. Finally, we provide detailed tutorials for preprocessing raw data, extracting stimulus features, and running encoding analyses that can serve as a pedagogical resource or a springboard for new research.</p>

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The “Podcast” ECoG dataset for modeling neural activity during natural language comprehension

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月3日

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  • Zaid Zada,
  • Samuel A. Nastase,
  • Bobbi Aubrey,
  • Itamar Jalon,
  • Sebastian Michelmann,
  • Haocheng Wang,
  • Liat Hasenfratz,
  • Werner Doyle,
  • Daniel Friedman,
  • Patricia Dugan,
  • Lucia Melloni,
  • Sasha Devore,
  • Adeen Flinker,
  • Orrin Devinsky,
  • Ariel Goldstein,
  • Uri Hasson

摘要

Naturalistic electrocorticography (ECoG) data are a rare but essential resource for studying the brain’s linguistic capabilities. ECoG offers high temporal resolution suitable for investigating processes at multiple temporal timescales and frequency bands. It also provides broad spatial coverage, often along critical language areas. Here, we share a dataset of nine ECoG participants with 1,330 electrodes listening to a 30-minute audio podcast. The richness of this naturalistic stimulus can be used for various research questions, from auditory perception to narrative integration. In addition to the neural data, we extracted linguistic features of the stimulus ranging from phonetic information to large language model word embeddings. We use these linguistic features in encoding models that relate stimulus properties to neural activity. Finally, we provide detailed tutorials for preprocessing raw data, extracting stimulus features, and running encoding analyses that can serve as a pedagogical resource or a springboard for new research.