Seedat Alia, Lepauvre Alex, Jeschke Jay, Gorska-Klimowska Urszula, Armendariz Marcelo, Bendtz Katarina, Henin Simon, Hirschhorn Rony, Brown Tanya, Jensen Erika, Kozma Csaba, Mazumder David, Montenegro Stephanie, Yu Leyao, Bonacchi Niccolò, Das Diptyajit, Kahraman Kyle, Sripad Praveen, Taheriyan Fatemeh, Devinsky Orrin, Dugan Patricia, Doyle Werner, Flinker Adeen, Friedman Daniel, Lake Wendell, Pitts Michael, Mudrik Liad, Boly Melanie, Devore Sasha, Kreiman Gabriel, Melloni Lucia
Department of Neurology, New York University Grossman School of Medicine, New York, NY, 10016, USA.
Neural Circuits, Consciousness and Cognition Research Group, Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, 60322, Germany.
Sci Data. 2025 May 23;12(1):854. doi: 10.1038/s41597-025-04833-z.
We introduce an intracranial EEG (iEEG) dataset collected as part of an adversarial collaboration between proponents of two theories of consciousness: Global Neuronal Workspace Theory and Integrated Information Theory. The data were recorded from 38 patients undergoing intracranial monitoring of epileptic seizures across three research centers using the same experimental protocol. Participants were presented with suprathreshold visual stimuli belonging to four different categories (faces, objects, letters, false fonts) in three orientations (front, left, right view), and for three durations (0.5, 1.0, 1.5 s). Participants engaged in a non-speeded Go/No-Go target detection task to identify infrequent targets with some stimuli becoming task-relevant and others task-irrelevant. Participants also engaged in a motor localizer task. The data were checked for its quality and converted to Brain Imaging Data Structure (BIDS). The de-identified dataset contains demographics, clinical information, electrode reconstruction, behavioral performance, and eye-tracking data. We also provide code to preprocess and analyze the data. This dataset holds promise for reuse in consciousness science and vision neuroscience to answer questions related to stimulus processing, target detection, and task-relevance, among many others.
我们介绍了一个颅内脑电图（iEEG）数据集，该数据集是作为意识的两种理论（全局神经元工作空间理论和整合信息理论）的支持者之间的对抗性合作的一部分而收集的。数据是在三个研究中心，按照相同的实验方案，从38名接受癫痫发作颅内监测的患者身上记录的。向参与者呈现属于四个不同类别的阈上视觉刺激（面部、物体、字母、假字体），呈现方向有三种（正视图、左视图、右视图），持续时间有三种（0.5、1.0、1.5秒）。参与者进行了一项非快速的Go/No-Go目标检测任务，以识别不常见的目标，其中一些刺激成为任务相关刺激，另一些则为任务无关刺激。参与者还进行了一项运动定位任务。对数据的质量进行了检查，并将其转换为脑成像数据结构（BIDS）。经过身份识别处理的数据集包含人口统计学信息、临床信息、电极重建、行为表现和眼动追踪数据。我们还提供了预处理和分析数据的代码。这个数据集有望在意识科学和视觉神经科学中被重新使用，以回答与刺激处理、目标检测和任务相关性等诸多问题。
