Article(id=1308463019441279880, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250257, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1761148800000, revisedDateStr=2025-10-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891493902, onlineDateStr=2026-09-20, pubDate=1778774400000, pubDateStr=2026-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891493902, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891493902, creator=13701087609, updateTime=1789891493902, updator=13701087609, issue=Issue{id=1308463017016972142, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='5', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1778774400000', pubDateStr='2026-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891493324, creator='13701087609', updateTime=1789892866211, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468775385322487, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468775385322488, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=42, endPage=48, ext={EN=ArticleExt(id=1308463019634217865, articleId=1308463019441279880, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=A Study on the Alleviation of Motion Sickness Through Olfactory and Auditory Stimulation Based on EEG Features, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the issue of motion sickness among passengers in autonomous driving scenarios, which significantly compromises the travel experience, this study proposes an olfactory and auditory mitigation method based on EEG analysis. Using mint gas and alpha brainwave music as stimuli, an autonomous driving environment is constructed via a driving simulation platform. EEG signals are collected from 30 participants, and three motion sickness mitigation protocols (auditory, olfactory, and combined auditory-olfactory) are designed. Combining temporal and frequency domain features (mean, variance, skewness, kurtosis, power spectral density) from EEG signals with classification algorithms, an EEG-based motion sickness recognition model is developed. This model is then used to evaluate the efficacy of the three approaches in alleviating motion sickness. Results indicate: The auditory & olfactory protocol demonstrated the highest efficacy, reducing Relief Index (RI) by an average of 58%. The olfactory-only protocol showed moderate efficacy, reducing RI by an average of 35%. The auditory-only protocol exhibited the lowest efficacy, reducing RI by an average of 21%.

, authors=Zhen Fan^{1, 2}, Bangbei Tang^{2, 3}, Yan Li², Bingjie Luo², Huoping Lu², authorsList=Zhen Fan, Bangbei Tang, Yan Li, Bingjie Luo, Huoping Lu, authorCompany=null, correspAuthors=Bangbei Tang, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308463019843933066, articleId=1308463019441279880, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于脑电特征的嗅觉与听觉对晕动症的缓解研究, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对自动驾驶条件下乘员易发生晕动症，严重影响出行体验的问题，提出一种基于脑电图分析的嗅觉与听觉缓解方法。以薄荷气体和α脑波音乐作为刺激源，通过驾驶模拟平台构建自动驾驶环境，采集30名被试者的脑电信号，设计了听觉、嗅觉、听觉&嗅觉3种晕动症缓解方案，结合脑电信号中的时域、频域特征（平均值、方差、偏度、峰度、功率谱密度）与分类算法构建了基于脑电数据的晕动症识别模型，并基于该模型进一步评估了3种方案对晕动症的缓解效果。结果表明：听觉&嗅觉方案缓解效果最好，缓解指数（RI）平均值达到58%；单一嗅觉方案的缓解效果次之，缓解指数平均值为35%；单一听觉方案的缓解效果最差，缓解指数平均值为21%。

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