Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  百年期刊
Journal of Tsinghua University(Science and Technology)    2020, Vol. 60 Issue (12) : 993-998     DOI: 10.16511/j.cnki.qhdxxb.2020.25.001
Mechanical Engineering |
EEG characteristics of motion sickness subjects in automatic driving mode based on virtual reality tests
Leilei ZHAO1,2,Chong LI1,*(),Linhong JI1,Tieniu YANG2
1. Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
2. Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
Download: PDF(1288 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Motion sickness is a common problem when traveling. Research has shown that nearly 1/3 of the population suffers from motion sickness when travelling by sea, land and air. The pathogenesis of motion sickness is complex with no unified conclusions. The sensory conflict hypothesis holds that motion sickness is mainly caused by mismatches of vestibule, vision and proprioception. Since a driver does not need to operate a car when using automatic driving, the feeling conflict is intensified and the automatic driving is less comfortable. This study investigates physiological indexes which can be used to quantitatively evaluate motion sickness. The experiments used a 6-DOF simulator platform combined with a virtual reality (VR) system to simultaneously provide visual and vestibular stimulation to the subjects. The subjective motion scores and EEG (electroencephalogram) signals of 11 healthy subjects were recorded during automatic driving and active driving scenarios to compare the motion responses of the subjects during the two driving scenarios. Analyses of the motion scores and the EEG records show that the subjects' motion scores were 2 points higher during automatic driving than during active driving with increases of motion sickness related to increases in the gravity frequency based on the power spectral density of the θ waves in the motor center (FC2, Cz), sensory center (CP5, P3) and visual center (POz) of the brain during automatic driving. The paired t test showed correlation between the gravity frequency differences based on the power spectral density of the subject during active driving and automatic driving (p < 0.05). The results indicate that subjects are more likely to develop motion sickness during automatic driving and that EEG signals can be used to quantitatively evaluate the degree of motion sickness.

Keywords automatic driving      motion sickness      EEG      gravity frequency     
Corresponding Authors: Chong LI     E-mail: chongli@tsinghua.edu.cn
Issue Date: 14 October 2020
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Leilei ZHAO
Chong LI
Linhong JI
Tieniu YANG
Cite this article:   
Leilei ZHAO,Chong LI,Linhong JI, et al. EEG characteristics of motion sickness subjects in automatic driving mode based on virtual reality tests[J]. Journal of Tsinghua University(Science and Technology), 2020, 60(12): 993-998.
URL:  
http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2020.25.001     OR     http://jst.tsinghuajournals.com/EN/Y2020/V60/I12/993
  
  
  
10.16511/j.cnki.qhdxxb.2020.25.001.T001

S2受试者主动驾驶和自动驾驶EEG重心频率的配对t检验

S2受试者EEG导联 θ波重心频率/Hz
F3 0.03
FC2 0.02
Cz 0.01
CP5 0.01
P3 0.01
POz 0.01
  
10.16511/j.cnki.qhdxxb.2020.25.001.T002

S2受试者主观评分和EEG重心频率的相关性

S2受试者EEG导联 θ波重心频率/Hz
主动驾驶 自动驾驶
F3 0.7 0.9
FC2 0.8 0.9
Cz 0.9 0.9
CP5 0.8 0.7
P3 0.7 0.9
POz 0.9 0.8
  
1 杨天使.基于MRI的晕动敏感个体差异与脑结构及功能连接的相关性研究[D].西安:西安电子科技大学, 2017.
1 YANG T S. Roles of brain structure and functional connectivity in the individual differences in motion sickness based on MRI[D]. Xi'an: Xidian University, 2017. (in Chinese)
3 STOFFREGEN T A , CHANG C H , CHEN F C , et al. Effects of decades of physical driving on body movement and motion sickness during virtual driving[J]. PLoS One, 2017. 12 (11): e0187120.
doi: 10.1371/journal.pone.0187120
4 WADA T , YOSHIDA K . Effect of passengers' active head tilt and opening/closure of eyes on motion sickness in lateral acceleration environment of cars[J]. Ergonomics, 2016. 59 (8): 1050- 1059.
doi: 10.1080/00140139.2015.1109713
5 WADA T , FUJISAWA S , DOI S . Analysis of driver's head tilt using a mathematical model of motion sickness[J]. International Journal of Industrial Ergonomics, 2018. 63, 89- 97.
doi: 10.1016/j.ergon.2016.11.003
6 ISKANDER J , ATTIA M , SALEH K , et al. From car sickness to autonomous car sickness:A review[J]. Transportation Research Part F:Traffic Psychology and Behaviour, 2019. 62, 716- 726.
doi: 10.1016/j.trf.2019.02.020
8 CHELEN W E , KABRISKY M , ROGERS S K . Spectral analysis of the electroencephalographic response to motion sickness[J]. Aviation, Space, and Environmental Medicine, 1993. 64 (1): 24- 29.
url: https://www.ncbi.nlm.nih.gov/pubmed/8424736
9 KIM Y Y , KIM H J , KIM E N , et al. Characteristic changes in the physiological components of cybersickness[J]. Psychophysiology, 2005. 42 (5): 616- 625.
url: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1469-8986.2005.00349.x
10 LIN Y T , CHIEN Y Y , WANG H H , et al. The quantization of cybersickness level using EEG and ECG for virtual reality head-mounted display[J]. Society for Information Display Symposium Digest of Technical Papers, 2018. 49 (1): 862- 865.
doi: 10.1002/sdtp.12267
11 CHEN Y C , DUANN J R , CHUANG S W , et al. Spatial and temporal EEG dynamics of motion sickness[J]. Neuro Image, 2010. 49 (3): 2862- 2870.
url: http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=8335123ea11e9d281424b3b1936f1126
12 徐苗.基于EEG的视觉诱导晕动症评估初步研究[D].重庆:重庆大学, 2017.
12 XU M. Preliminary study on EEG-based evaluation of visually induced motion sickness[D]. Chongqing: Chongqing University, 2017. (in Chinese)
13 WOOD C D , STEWART J J , WOOD M J , et al. Habituation and motion sickness[J]. Clinical Pharmacology, 1994. 34 (6): 628- 634.
doi: 10.1002/j.1552-4604.1994.tb02016.x
14 BLAND B H , ODDIE S D . Theta band oscillation and synchrony in the hippocampal formation and associated structures:The case for its role in sensorimotor integration[J]. Behavioural Brain Research, 2001. 127 (1-2): 119- 136.
doi: 10.1016/S0166-4328(01)00358-8
15 JENSEN O . Information transfer between rhythmically coupled networks:Reading the hippocampal phase code[J]. Neural Computation, 2001. 13 (12): 2743- 2761.
doi: 10.1162/089976601317098510
[1] CAO Xinying, ZHENG Decheng, QIN Peicheng, LI Xiaodong. Impact of construction industrial noise on workers' learning efficiency: A study based on electroencephalogram analysis[J]. Journal of Tsinghua University(Science and Technology), 2024, 64(2): 189-197.
[2] ZHANG Shousheng, ZHUANG Tengfei, FANG Xingxing, ZHU Hua, TANG Wei. Depth recognition thresholds of tactile perception for fine stripe texture of bar shapes[J]. Journal of Tsinghua University(Science and Technology), 2024, 64(1): 135-145.
[3] Chien-kai WANG,Fangfang WU,Yazheng DI,Jie ZHANG,Chong LI,Linhong JI. Effect of transcranial direct current stimulation on visual attention span[J]. Journal of Tsinghua University(Science and Technology), 2020, 60(12): 999-1006.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Copyright © Journal of Tsinghua University(Science and Technology), All Rights Reserved.
Powered by Beijing Magtech Co. Ltd