Chanoine Valérie, Todorović Snežana, Nazarian Bruno, Badier Jean-Michel, Kanzari Khoubeib, Brovelli Andrea, Kotz Sonja A, Runnqvist Elin
Aix Marseille Univ, CNRS, LPL, Aix-en-Provence, France.
Aix-Marseille Univ, ILCB, Aix-en-Provence, France.
Sci Data. 2025 May 14;12(1):792. doi: 10.1038/s41597-025-05127-0.
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.
“MEG-GLOUPS”数据集提供了一组经过整理的原始脑磁图记录，这些记录来自17名参与伪词学习任务的法国参与者，以及任务前后的静息态活动。一个名为Gloups的数据集已经可用，该数据集包含相同的参与者，并采用了适用于功能磁共振成像的类似学习任务。在学习任务中，参与者被要求读出单音节伪词，这些伪词通过视觉和听觉两种方式呈现。这些伪词在参与者的母语法语中，要么在语音结构上合法，要么不合法。我们根据脑成像数据结构（BIDS）对数据集进行了组织，对数据进行了预处理，并对事件相关场（ERF）进行了最小限度的分析，以确保数据集的数据质量和完整性。这个数据收集包括对理论背景、方法、数据记录和技术验证的全面描述。
