A multi-subject and multi-session EEG dataset for modelling human visual object recognition

• Published in: Scientific data Vol. 12; no. 1; pp. 663 - 15
• Main Authors: Xue, Shuning, Jin, Bu, Jiang, Jie, Guo, Longteng, Zhou, Jin, Wang, Changyong, Liu, Jing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/2613/2616; 631/378/2649/1723; Algorithms; Brain-Computer Interfaces; Data Descriptor; Datasets; EEG; Electroencephalography; Humanities and Social Sciences; Humans; Machine Learning; multidisciplinary; Pattern recognition; Pattern Recognition, Visual; Science; Science (multidisciplinary); Visual Perception
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-025-04843-x

We share a multi-subject and multi-session (MSS) dataset with 122-channel electroencephalographic (EEG) signals collected from 32 human participants. The data was obtained during serial visual presentation experiments in two paradigms. Dataset of first paradigm consists of around 800,000 trials presenting stimulus sequences at 5 Hz. Dataset of second paradigm comprises around 40,000 trials displaying each image for 1 second. Each participant completed between 1 to 5 sessions on different days, and each session lasted for approximately 1.5 hours of EEG recording. The stimulus set used in the experiments included 10,000 images, with 500 images per class, manually selected from PASCAL and ImageNet image databases. The MSS dataset can be useful for various studies, including but not limited to (1) exploring the characteristics of EEG visual response, (2) comparing the differences in EEG response of different visual paradigms, and (3) designing machine learning algorithms for cross-subject and cross-session brain-computer interfaces (BCIs) using EEG data from multiple subjects and sessions.