Evidence map›Paper›PMID 39001171›Full record

ArticleSensors (Basel, Switzerland)2024

An Identification Method for Road Hypnosis Based on Human EEG Data.

Bin Wang, Jingheng Wang, Xiaoyuan Wang, Longfei Chen, Han Zhang, Chenyang Jiao, Gang Wang, Kai Feng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

8 authors.

Bin WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Jingheng WangDepartment of Mathematics, Ohio State University, Columbus, OH 43220, USA.
Xiaoyuan WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0003-2418-7394
Longfei ChenCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0002-0065-1001
Han ZhangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Chenyang JiaoCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Gang WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0002-8684-6896
Kai FengCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.

Funding

the New Generation of Information Technology Innovation Project of China University Innova-tion Fund of Ministry of Education No. 2022IT191
6 · The paper itself

Abstract

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

Indexed as

Automobile DrivingElectroencephalographyHypnosisNeural Networks, ComputerAccidents, TrafficHumansdriversEEGroad hypnosisstate identificationvehicle

Identifiers

PMID39001171
PMCPMC11244404

What OpenQuestion holds

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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.