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An EEG dataset for understanding driving expertise from naturalistic urban road experiments.

Overview

Authors: Jiangtao Gong1, Yueteng Yu2, Yancheng Cao3, Ruoxuan Yang4, Xiang Chang3, Haoming Tang3, Xiaoji Zheng3, Yiyao Liu3, Shanhe You3, Chen Zheng3, Guyue Zhou3
ORCID iDs: Jiangtao Gong, Yueteng Yu, Xiang Chang, Yiyao Liu
  1. School of Design, Shanghai Jiao Tong University, Shanghai, 200240 China
  2. Queensland University of Technology, Brisbane, QLD 4000 Australia
  3. Institute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084 China
  4. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
Institutions: Shanghai Jiao Tong University (China); Queensland University of Technology (Australia); Tsinghua University (China); University of Hong Kong (Hong Kong SAR China)
Journal: Scientific data, volume 13, issue 1, article 884
Dates: received 31 July 2025; accepted 7 April 2026; published online 16 April 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07223-1 · PMID 41991943 · PMCID PMC13260947 · OpenAlex W7154576189
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), human (organism), methods / tools (subfield)
Methods: Preprocessing, Evoked potentials, Physiology & signal measures, Smoothing, state filtering, decompositions
Keywords: Cognitive neuroscience, Electrical and electronic engineering
MeSH: Automobile Driving*, Electroencephalography*, Algorithms, Decision Making, Humans (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: the Beijing Natural Science Foundation
Citations: cited by 1 paper (Europe PMC); 24 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

The paper links to its data, not to its authors' code: see the Data section.

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41597-026-07223-1.

Tracing map

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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41597-026-07223-1.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 5 MeSH terms, 1 funder, 24 references.

Cite

This paper

Gong, J., Yu, Y., Cao, Y., Yang, R., Chang, X., Tang, H., Zheng, X., Liu, Y., You, S., Zheng, C., & Zhou, G. (2026). An EEG dataset for understanding driving expertise from naturalistic urban road experiments. Scientific data, 13(1), 884. https://doi.org/10.1038/s41597-026-07223-1

BibTeX

@article{gong2026eeg,
author = {Gong, Jiangtao and Yu, Yueteng and Cao, Yancheng and Yang, Ruoxuan and Chang, Xiang and Tang, Haoming and Zheng, Xiaoji and Liu, Yiyao and You, Shanhe and Zheng, Chen and Zhou, Guyue},
title = {{An EEG dataset for understanding driving expertise from naturalistic urban road experiments}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {884},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07223-1},
url = {https://doi.org/10.1038/s41597-026-07223-1},
pmid = {41991943},
pmcid = {PMC13260947}
}

RIS

TY - JOUR
AU - Gong, Jiangtao
AU - Yu, Yueteng
AU - Cao, Yancheng
AU - Yang, Ruoxuan
AU - Chang, Xiang
AU - Tang, Haoming
AU - Zheng, Xiaoji
AU - Liu, Yiyao
AU - You, Shanhe
AU - Zheng, Chen
AU - Zhou, Guyue
TI - An EEG dataset for understanding driving expertise from naturalistic urban road experiments
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/16
VL - 13
IS - 1
SP - 884
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07223-1
UR - https://doi.org/10.1038/s41597-026-07223-1
LA - en
ER -

CSL-JSON

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