{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T07:07:07Z","timestamp":1785654427961,"version":"3.56.0"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Physiology & Behavior"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.physbeh.2024.114626","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T17:28:22Z","timestamp":1719941302000},"page":"114626","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":8,"special_numbering":"C","title":["Detection of virtual reality motion sickness based on EEG using asymmetry of entropy and cross-frequency coupling"],"prefix":"10.1016","volume":"284","author":[{"given":"Chengcheng","family":"Hua","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lining","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanfeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianlong","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongrong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.physbeh.2024.114626_bib0001","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s10055-016-0285-9","article-title":"Review on cybersickness in applications and visual displays[J]","volume":"20","author":"Rebenitsch","year":"2016","journal-title":"Virtual. Real."},{"key":"10.1016\/j.physbeh.2024.114626_bib0002","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.displa.2019.07.002","article-title":"Cybersickness during VR gaming undermines game enjoyment: a mediation model[J]","volume":"59","author":"Yildirim","year":"2019","journal-title":"Displays"},{"issue":"1","key":"10.1016\/j.physbeh.2024.114626_bib0003","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1177\/00187208211008687","article-title":"The visually induced motion sickness susceptibility questionnaire(vimssq): estimating individual susceptibility to motion sickness-like symptoms when using visual devices[J]","volume":"65","author":"Keshavarz","year":"2023","journal-title":"Hum. Factors"},{"issue":"20","key":"10.1016\/j.physbeh.2024.114626_bib0004","first-page":"43","article-title":"Analysis of VR-induced motion sickness based on entropy power spectrum of EEG samples[J]","volume":"45","author":"Chai","year":"2022","journal-title":"Electronic Meas. Tech."},{"key":"10.1016\/j.physbeh.2024.114626_bib0005","doi-asserted-by":"crossref","first-page":"46389","DOI":"10.1109\/ACCESS.2019.2909035","article-title":"Feature extraction and simulation of EEG signals during exercise-induced fatigue[J]","volume":"7","author":"Yang","year":"2019","journal-title":"IEEe Access."},{"issue":"15","key":"10.1016\/j.physbeh.2024.114626_bib0006","doi-asserted-by":"crossref","first-page":"12527","DOI":"10.1007\/s00521-022-07292-4","article-title":"Human emotion recognition from EEG-based brain-computer interface using machine learning: a comprehensive review[J]","volume":"34","author":"Houssein","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.physbeh.2024.114626_bib0007","article-title":"Human emotion recognition based on time-frequency analysis of multivariate EEG signal[J]","volume":"238","author":"Padhmashree","year":"2022","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"10.1016\/j.physbeh.2024.114626_bib0008","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10055-021-00517-2","article-title":"Quantifying VR cybersickness using EEG[J]","volume":"26","author":"Krokos","year":"2022","journal-title":"Virtual Real."},{"issue":"06","key":"10.1016\/j.physbeh.2024.114626_bib0009","first-page":"35","article-title":"EEG characterization of visually induced motion sickness[J]","volume":"19","author":"Qin","year":"2021","journal-title":"Life Sci. Instrumentation"},{"key":"10.1016\/j.physbeh.2024.114626_bib0010","series-title":"2019 International seminar on intelligent technology and its applications (ISITIA)","first-page":"325","article-title":"Eeg visualization for cybersickness detection during playing 3d video games[C]","author":"Wibawa","year":"2019"},{"issue":"4","key":"10.1016\/j.physbeh.2024.114626_bib0011","doi-asserted-by":"crossref","DOI":"10.3390\/s22041314","article-title":"Cybersickness and Its Severity Arising from Virtual Reality Content: a Comprehensive Study[J]","volume":"22","author":"Oh","year":"2022","journal-title":"Sensors. (Basel)"},{"key":"10.1016\/j.physbeh.2024.114626_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.neulet.2020.135589","article-title":"Test-retest reliability of the virtual reality sickness evaluation using electroencephalography (EEG)[J]","volume":"743","author":"Lim","year":"2021","journal-title":"Neurosci. Lett."},{"issue":"02","key":"10.1016\/j.physbeh.2024.114626_bib0013","first-page":"93","article-title":"Fault diagnosis method for automata based on parameter optimization VMD and fuzzy entropy[J]","volume":"38","author":"Liu","year":"2022","journal-title":"Mech. Eng. Res."},{"issue":"23","key":"10.1016\/j.physbeh.2024.114626_bib0014","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1109\/TSP.2019.2951223","article-title":"Multivariate variational mode decomposition[J]","volume":"67","author":"Ur Rehman","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"issue":"6","key":"10.1016\/j.physbeh.2024.114626_bib0015","first-page":"853","article-title":"A multidomain feature extraction method for EEG based on multivariate variational mode decomposition[J]","volume":"33","author":"Meng","year":"2020","journal-title":"J. Remote Sens."},{"key":"10.1016\/j.physbeh.2024.114626_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104237","article-title":"Recognising drivers\u2019 mental fatigue based on EEG multi-dimensional feature selection and fusion[J]","volume":"79","author":"Zhang","year":"2023","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.physbeh.2024.114626_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.neubiorev.2022.104690","article-title":"Cross-frequency coupling in psyc-hiatric disorders: a systematic review[J]","volume":"138","author":"Yakubov","year":"2022","journal-title":"Neurosci. Biobehav. Rev."},{"key":"10.1016\/j.physbeh.2024.114626_bib0018","series-title":"2011 Annual International Confer-ence of the IEEE Engineering in Medicine and Biology Society","first-page":"1632","article-title":"Probing cortical excita-bility using cross-frequency coupling in intracranial EEG recordings: a new method for seizure prediction[C]","author":"Alvarado-Rojas","year":"2011"},{"key":"10.1016\/j.physbeh.2024.114626_bib0019","series-title":"2009 Sixth International Conference on Computer Graphics, Imaging an-d Visualization","first-page":"486","article-title":"Estimating Cybersickness of Simulated Motion Us-ing the Simulator Sickness Questionnaire (SSQ): a Controlled Study[C]","author":"Bruck","year":"2009"},{"key":"10.1016\/j.physbeh.2024.114626_bib0020","article-title":"Study on the influence of feature extraction on channel selection method[J]","author":"Zhang","year":"2020","journal-title":"Foreign electronic measurement technology"},{"issue":"12","key":"10.1016\/j.physbeh.2024.114626_bib0021","first-page":"74","article-title":"Fault diagnosis of rolling bearings based on multivariate variational modal decomposition and 1.5-dimensional spectrum[J]","volume":"517","author":"Tang","year":"2022","journal-title":"Bearing"},{"key":"10.1016\/j.physbeh.2024.114626_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103209","article-title":"Decoding SSVEP patterns from EEG via multivariate variational mode decomposition-informed canonical correlation analysis[J]","volume":"71","author":"Chang","year":"2022","journal-title":"Biomed. Signal. Process. Control"},{"issue":"10","key":"10.1016\/j.physbeh.2024.114626_bib0023","first-page":"1284","article-title":"Research on early fault feature extraction of rolling bearing based on MVMD-FRFT[J]","volume":"38","author":"Hong","year":"2021","journal-title":"Mech. Electrical Engr."},{"key":"10.1016\/j.physbeh.2024.114626_bib0024","first-page":"1","article-title":"Brain activity during cybersickness: a s-coping review[J]","author":"Chang","year":"2023","journal-title":"Virtual. Real."},{"issue":"3","key":"10.1016\/j.physbeh.2024.114626_bib0025","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1007\/s11357-022-00552-0","article-title":"Analysis of complexity in the EEG activity of Parkin-son's disease patients by means of approximate entropy[J]","volume":"44","author":"Pappalettera","year":"2022","journal-title":"Geroscience"},{"issue":"8","key":"10.1016\/j.physbeh.2024.114626_bib0026","doi-asserted-by":"crossref","first-page":"201","DOI":"10.3390\/brainsci9080201","article-title":"EEG signals feature extraction based on DWT and EMD combined with approximate entropy[J]","volume":"9","author":"Ji","year":"2019","journal-title":"Brain Sci."},{"key":"10.1016\/j.physbeh.2024.114626_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2019.101711","article-title":"Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks[J]","volume":"102","author":"Gao","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.physbeh.2024.114626_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2021.103417","article-title":"Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies[J]","volume":"73","author":"Shoeibi","year":"2022","journal-title":"Biomed. Signal. Process. Control"},{"key":"10.1016\/j.physbeh.2024.114626_bib0029","doi-asserted-by":"crossref","first-page":"39926","DOI":"10.1109\/ACCESS.2022.3165199","article-title":"Complexity analysis of EEG in patients with social anxiety disorder using fuzzy entropy and machine learning techniques[J]","volume":"10","author":"Al-Ezzi","year":"2022","journal-title":"IEEe Access."},{"issue":"1","key":"10.1016\/j.physbeh.2024.114626_bib0030","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s10548-022-00927-8","article-title":"Effects of pharmacological treatments in alzheimer\u2019-s disease: permutation e-ntropy based EEG complexity study[J]","volume":"36","author":"Fide","year":"2023","journal-title":"Brain Topogr."},{"key":"10.1016\/j.physbeh.2024.114626_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106116","article-title":"Complexity of EEG dynamics for early diagnosis of Alzheimer's disease using permutation entropy neuromarker[J]","volume":"206","author":"\u015eeker","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"10.1016\/j.physbeh.2024.114626_bib0032","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/abf773","article-title":"An automatic sleep disorder detection based on EEG cross-f-requency coupling and random forest model[J]","volume":"18","author":"Dimitriadis","year":"2021","journal-title":"J. Neural Eng."},{"key":"10.1016\/j.physbeh.2024.114626_bib0033","series-title":"2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)","first-page":"140","article-title":"Phase amplitude coupling of theta-gamma EEG frequency bands in sleep apnoea[C]","author":"Abdullah","year":"2015"},{"key":"10.1016\/j.physbeh.2024.114626_bib0034","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/RBME.2020.2969915","article-title":"A review on machine learning for EEG signal processing in bioengineering[J]","volume":"14","author":"Hosseini","year":"2020","journal-title":"IEEe Rev. Biomed. Eng."},{"issue":"12","key":"10.1016\/j.physbeh.2024.114626_bib0035","first-page":"2469","article-title":"EEG signal classification method for pilepsy based on variational modal decomposition[J]","volume":"48","author":"Zhang","year":"2020","journal-title":"Acta Electronica Sinica"}],"container-title":["Physiology & Behavior"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031938424001719?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031938424001719?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T23:09:59Z","timestamp":1728342599000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031938424001719"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":35,"alternative-id":["S0031938424001719"],"URL":"https:\/\/doi.org\/10.1016\/j.physbeh.2024.114626","relation":{},"ISSN":["0031-9384"],"issn-type":[{"value":"0031-9384","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Detection of virtual reality motion sickness based on EEG using asymmetry of entropy and cross-frequency coupling","name":"articletitle","label":"Article Title"},{"value":"Physiology & Behavior","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.physbeh.2024.114626","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114626"}}