EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation.

Journal: Computer methods and programs in biomedicine
PMID: 38728828

Abstract

BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue.

Authors

  • Chengcheng Hua
    * Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China.
  • Jianlong Tao
    School of Automation, C-IMER, CICAEET, Nanjing University of Information Science & Technology, Nanjing 210044, China.
  • Zhanfeng Zhou
    School of Automation, C-IMER, CICAEET, Nanjing University of Information Science & Technology, Nanjing 210044, China.
  • Lining Chai
    School of Automation, C-IMER, CICAEET, Nanjing University of Information Science & Technology, Nanjing 210044, China.
  • Ying Yan
    School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang, Guizhou, China.
  • Jia Liu
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Rongrong Fu
    Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Department of Electrical Engineering, Yanshan University, Qinhuangdao 066000, China. Electronic address: frr1102@aliyun.com.

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