Zada Zaid, Nastase Samuel A, Aubrey Bobbi, Jalon Itamar, Michelmann Sebastian, Wang Haocheng, Hasenfratz Liat, Doyle Werner, Friedman Daniel, Dugan Patricia, Melloni Lucia, Devore Sasha, Flinker Adeen, Devinsky Orrin, Goldstein Ariel, Hasson Uri
Princeton Neuroscience Institute and Department of Psychology, Princeton University, New Jersey, 08544, USA.
Grossman School of Medicine, New York University, New York, 10016, USA.
Sci Data. 2025 Jul 3;12(1):1135. doi: 10.1038/s41597-025-05462-2.
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.
自然主义脑电描记术（ECoG）数据是研究大脑语言能力的一种稀缺但重要的资源。ECoG具有高时间分辨率，适用于研究多个时间尺度和频段上的过程。它还提供广泛的空间覆盖范围，通常覆盖关键语言区域。在这里，我们分享了一个数据集，该数据集来自9名ECoG参与者，他们使用1330个电极收听了一段30分钟的音频播客。这种自然主义刺激的丰富性可用于各种研究问题，从听觉感知到叙事整合。除了神经数据，我们还提取了刺激的语言特征，范围从语音信息到大型语言模型词嵌入。我们在将刺激属性与神经活动相关联的编码模型中使用这些语言特征。最后，我们提供了预处理原始数据、提取刺激特征和运行编码分析的详细教程，这些教程可作为教学资源或新研究的跳板。
