Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub‐classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi‐channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.

中文翻译：

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使用脑电图在不舒服的高温和舒适的环境之间连续区分个人的热舒适感。

热舒适性是建筑物设计的重要因素。尽管已经充分认识到许多生理参数与人的热舒适或不舒适状态有关，但是如何使用生理信号来判断热舒适状态还没有得到很好的研究。在本文中，通过在私人空间中使用脑电图（EEG）信号讨论了连续确定个人热舒适感的可行性。在这项研究中，有22名受试者暴露在热舒适和不舒适的高温环境中，并记录了他们的EEG信号。提取脑电信号的频谱功率特征，并使用线性判别分析或支持向量机作为子分类器的集成学习方法建立判别模型。结果表明，在60秒的检测窗口内可获得87.9％的平均识别精度。这项研究表明，通过不中断的多通道EEG信号来区分一个人在自己的私人空间中感到舒适还是过热是可行的，并提出了进一步应用于神经人体工程学的可能性。