ArticleScientific data2025
The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Constituent-constrained word prediction during language comprehension.Nature neuroscience · 2026Article
- Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?The European journal of neuroscience · 2026Review
- Article
- Aligning brains into a shared space improves their alignment with large language models.Nature computational science · 2026Article
- Evaluating scientific theories as predictive models in language neuroscience.bioRxiv : the preprint server for biology · 2025Article
- The "Podcast" ECoG dataset for modeling neural activity during natural language comprehension.Scientific data · 2025Article
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16 authors.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.