脑电图
高速列车
机制(生物学)
模拟
工作(物理)
工程类
计算机科学
运输工程
心理学
哲学
认识论
精神科
机械工程
作者
Yong Peng,Yating Lin,Chaojie Fan,Qian Xu,Diya Xu,Shengen Yi,Honghao Zhang,Kui Wang
标识
DOI:10.1016/j.buildenv.2021.108711
摘要
The overall comfort of train passenger is influenced by many environmental factors such as vibration, noise and pressure. However, the couple effect of these influencing factors causes the difficulty in evaluating the overall comfort. This study revealed the potential comfort degradation mechanisms in high-speed railway environments and proposed a machine learning evaluation model to assess passenger comfort. Here, the subjective overall comfort ratings and the electroencephalography (EEG) of twenty passengers were collected in the field tests. Compared with passengers who were in a state of comfort, the brain areas (BA6/13/20/24/31/40/47) of passengers who felt uncomfortable were all significantly activated in the beta band. Based on the neural signature above, three related human reactions when passengers feel uncomfortable were recognized, including perceiving the environment, inducing negative emotions, and finally producing body movement intention. To assess the overall comfort of train passengers, six kinds of features extracted from EEG signals were used to train an evaluation model based on the LightGBM algorithm. This work offers a neurological explanation for the mechanisms of degradation of overall comfort and provides a novel and effective method to assess it.
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