{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T15:10:11Z","timestamp":1788275411637,"version":"build-2803163510"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Unveiling & Leading Project of XZHMU","award":["JBGS202204"],"award-info":[{"award-number":["JBGS202204"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The surging popularity of virtual reality (VR) technology raises concerns about VR-induced motion sickness, linked to discomfort and nausea in simulated environments. Our method involves in-depth analysis of EEG data and user feedback to train a sophisticated deep learning model, utilizing an enhanced GRU network for identifying motion sickness patterns. Following comprehensive data pre-processing and feature engineering to ensure input accuracy, a deep learning model is trained using supervised and unsupervised techniques for classifying and predicting motion sickness severity. Rigorous training and validation procedures confirm the model\u2019s robustness across diverse scenarios. Research results affirm our deep learning model\u2019s 84.9% accuracy in classifying and predicting VR-induced motion sickness, surpassing existing models. This information is vital for improving the VR experience and advancing VR technology.<\/jats:p>","DOI":"10.1371\/journal.pone.0305733","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T17:29:42Z","timestamp":1721410182000},"page":"e0305733","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["Feature extraction method of EEG based on wavelet packet reconstruction and deep learning model of VR motion sickness feature classification and 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