Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception

Open multi-center intracranial electroencephalography dataset with task probing conscious visual perception

Abstract 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 monitori...

Full description

Saved in:
Bibliographic Details
Main Authors: Alia Seedat, Alex Lepauvre, Jay Jeschke, Urszula Gorska-Klimowska, Marcelo Armendariz, Katarina Bendtz, Simon Henin, Rony Hirschhorn, Tanya Brown, Erika Jensen, Csaba Kozma, David Mazumder, Stephanie Montenegro, Leyao Yu, Niccolò Bonacchi, Diptyajit Das, Kyle Kahraman, Praveen Sripad, Fatemeh Taheriyan, Orrin Devinsky, Patricia Dugan, Werner Doyle, Adeen Flinker, Daniel Friedman, Wendell Lake, Michael Pitts, Liad Mudrik, Melanie Boly, Sasha Devore, Gabriel Kreiman, Lucia Melloni
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-04833-z
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Abstract 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.
ISSN:2052-4463

Similar Items