Assessment of Virtual Reality Motion Sickness Severity Based on EEG via LSTM/BiLSTM-Reference-Cited by-同舟云学术

Assessment of Virtual Reality Motion Sickness Severity Based on EEG via LSTM/BiLSTM

Published:2023-10-15 Issue:20 Volume:23 Page:24839-24848
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Hua Chengcheng¹^ORCID,Chai Lining¹,Yan Ying¹^ORCID,Liu Jia¹^ORCID,Wang Qiaoxiu²,Fu Rongrong³^ORCID,Zhou Zhanfeng¹

Affiliation:

1. School of Automation, C-IMER, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China

2. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China

3. Department of Electrical Engineering, Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Yanshan University, Qinhuangdao, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology

Natural Science Foundation of Hebei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10286142/10242330.pdf?arnumber=10242330

Reference58 articles.

1. Evaluating Cybersickness of Walking on an Omnidirectional Treadmill in Virtual Reality

2. Are Modern Head-Mounted Displays Sexist? A Systematic Review on Gender Differences in HMD-Mediated Virtual Reality

3. A survey of challenges and methods for Quality of Experience assessment of interactive VR applications

4. Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network

5. Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning

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