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<p>The “MEG-GLOUPS” dataset offers a curated collection of raw magnetoencephalography recordings from seventeen French participants engaged in a pseudoword learning task as well as resting-state activity before and after the task. A dataset called Gloups with the same participants and a similar learning task adapted to functional magnetic resonance imaging is already available. In the learning task, participants were instructed to pronounce monosyllabic pseudowords, which were presented both visually and auditorily. These pseudowords were either phonotactically legal or illegal in the participants’ native language, French. We organized the dataset according to the Brain Imaging Data Structure (BIDS), pre-processed the data and performed a minimal analysis of Event-Related Fields (ERFs), to ensure data quality and integrity of the dataset. This data collection includes comprehensive descriptions of the theoretical background, methods, data recordings, and technical validation.</p>

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Dataset for Evaluating the Production of Phonotactically Legal and Illegal Pseudowords

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月14日

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  • Valérie Chanoine,
  • Snežana Todorović,
  • Bruno Nazarian,
  • Jean-Michel Badier,
  • Khoubeib Kanzari,
  • Andrea Brovelli,
  • Sonja A. Kotz,
  • Elin Runnqvist

摘要

The “MEG-GLOUPS” dataset offers a curated collection of raw magnetoencephalography recordings from seventeen French participants engaged in a pseudoword learning task as well as resting-state activity before and after the task. A dataset called Gloups with the same participants and a similar learning task adapted to functional magnetic resonance imaging is already available. In the learning task, participants were instructed to pronounce monosyllabic pseudowords, which were presented both visually and auditorily. These pseudowords were either phonotactically legal or illegal in the participants’ native language, French. We organized the dataset according to the Brain Imaging Data Structure (BIDS), pre-processed the data and performed a minimal analysis of Event-Related Fields (ERFs), to ensure data quality and integrity of the dataset. This data collection includes comprehensive descriptions of the theoretical background, methods, data recordings, and technical validation.