Evidence map›Paper›PMID 41991943›Full record

ArticleScientific data2026

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

Jiangtao Gong, Yueteng Yu, Yancheng Cao, Ruoxuan Yang, Xiang Chang, Haoming Tang, Xiaoji Zheng, Yiyao Liu, Shanhe You, Chen Zheng and 1 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jiangtao GongSchool of Design, Shanghai Jiao Tong University, Shanghai, 200240, China. gongjiangtao@sjtu.edu.cn.ORCID http://orcid.org/0000-0002-4310-1894
Yueteng YuQueensland University of Technology, Brisbane, QLD 4000, Australia.
Yancheng CaoInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.
Ruoxuan YangDepartment of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
Xiang ChangInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.
Haoming TangInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.
Xiaoji ZhengInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.
Yiyao LiuInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.
Shanhe YouInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.
Chen ZhengInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.
Guyue ZhouInstitute for AI Industry Research (AIR), Tsinghua University, Beijing, 100084, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern autonomous driving algorithms in complex urban environments strive for safety, comfort, and intelligence, qualities exemplified by expert drivers. This paper presents a comprehensive EEG dataset comparing 10 expert and 10 novice drivers in 13 naturalistic urban driving conditions on a fixed 5.7-kilometer route in in a city. Our multi-modal dataset synchronizes brain activity with vehicle CAN bus data, traffic participant information, and psychophysiological measures (Electrodermal Activity and Heart Rate) from drivers. Uniquely, we also collected physiological and subjective feedback from two passengers per trip to validate driving performance quality. All participants (drivers and passengers) completed a series of standardized subjective questionnaires at pre- and post-experiment. Finally, they participated in post-drive semi-structured interviews exploring driver decision-making processes and passenger experience. This novel dataset enables researchers to decode the neural signatures underlying driving expertise, providing valuable insights for developing more human-like, intelligent autonomous driving algorithms that can better navigate the complexities of urban traffic environments.

Indexed as

Automobile DrivingElectroencephalographyAlgorithmsDecision MakingHumans

Identifiers

PMID41991943
PMCPMC13260947

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.