Li Yilun, Li Yue, Li Yan, Luo Bingjie, Tang Bangbei, Yue Qizong
School of Music and Dance, Henan Institute of Science and Technology, Xinxiang, China.
School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, China.
Front Hum Neurosci. 2025 Sep 3;19:1636109. doi: 10.3389/fnhum.2025.1636109. eCollection 2025.
Motion sickness often causes passengers to experience negative emotions such as tension, which in turn triggers symptoms like dizziness and nausea, seriously affecting the travel experience of passengers. Previous studies have shown that music can alleviate negative emotions such as tension, but its effect on motion sickness remains unclear, and the differences in the alleviation effect of different types of music on motion sickness need to be quantitatively evaluated.
We collected Electroencephalogram (EEG) data from 30 subjects in a simulated driving environment and constructed a motion sickness recognition model by combining time-and frequency-domain features (mean, variance, skewness, kurtosis, power spectral density) with classification algorithms. The model achieved accurate identification of passenger motion sickness states. Based on this model, the intervention effects of four types of music (joyful, sad, stirring, and soft) on motion sickness were further evaluated and compared with the control group (taking natural recovery measures).
The results showed that soft and joyful music had better intervention effects (average reduction of 56.7 and 57.3%, respectively), followed by passionate and sad music (average reduction of 48.3 and 40%, respectively), among which the alleviation effect of sad music was lower than that of the control group (average reduction of 43.3%). In addition, it was verified that the EEG Kolmogorov-Chaitin complexity in the occipital region was significantly negatively correlated with the motion sickness grade = -0.625, < 0.005).
The study suggests that personalized music intervention strategies may effectively alleviate motion sickness symptoms of passengers, thereby increasing cabin comfort and improving the travel experience of passengers.
晕动病常使乘客产生紧张等负面情绪，进而引发头晕、恶心等症状，严重影响乘客的旅行体验。以往研究表明，音乐可缓解紧张等负面情绪，但其对晕动病的影响尚不清楚，不同类型音乐对晕动病缓解效果的差异有待定量评估。
我们在模拟驾驶环境中收集了30名受试者的脑电图（EEG）数据，并通过将时域和频域特征（均值、方差、偏度、峰度、功率谱密度）与分类算法相结合，构建了晕动病识别模型。该模型实现了对乘客晕动病状态的准确识别。基于此模型，进一步评估了四种类型音乐（欢快、悲伤、激昂、柔和）对晕动病的干预效果，并与对照组（采取自然恢复措施）进行比较。
结果表明，柔和与欢快的音乐具有更好的干预效果（平均降低率分别为56.7%和57.3%），其次是激昂和悲伤的音乐（平均降低率分别为48.3%和40%），其中悲伤音乐的缓解效果低于对照组（平均降低率为43.3%）。此外，还验证了枕叶区域的脑电图柯尔莫哥洛夫 - 柴廷复杂度与晕动病等级显著负相关（= -0.625，<0.005）。
该研究表明，个性化音乐干预策略可能有效缓解乘客的晕动病症状，从而提高机舱舒适度，改善乘客的旅行体验。
