Nayak Tapsya, Zhang Tinghe, Mao Zijing, Xu Xiaojing, Zhang Lin, Pack Daniel J, Dong Bing, Huang Yufei
Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA.
NSF-DOE CURRENT Center, University of Tennessee, Knoxville, TN 37996, USA.
Brain Sci. 2018 Apr 23;8(4):74. doi: 10.3390/brainsci8040074.
Varying indoor environmental conditions is known to affect office worker’s performance; wherein past research studies have reported the effects of unfavorable indoor temperature and air quality causing sick building syndrome (SBS) among office workers. Thus, investigating factors that can predict performance in changing indoor environments have become a highly important research topic bearing significant impact in our society. While past research studies have attempted to determine predictors for performance, they do not provide satisfactory prediction ability. Therefore, in this preliminary study, we attempt to predict performance during office-work tasks triggered by different indoor room temperatures (22.2 °C and 30 °C) from human brain signals recorded using electroencephalography (EEG). Seven participants were recruited, from whom EEG, skin temperature, heart rate and thermal survey questionnaires were collected. Regression analyses were carried out to investigate the effectiveness of using EEG power spectral densities (PSD) as predictors of performance. Our results indicate EEG PSDs as predictors provide the highest ² (> 0.70), that is 17 times higher than using other physiological signals as predictors and is more robust. Finally, the paper provides insight on the selected predictors based on brain activity patterns for low- and high-performance levels under different indoor-temperatures.
众所周知，室内环境条件的变化会影响办公室工作人员的表现；过去的研究报告了不利的室内温度和空气质量会导致办公室工作人员出现病态建筑综合症（SBS）。因此，研究在不断变化的室内环境中能够预测表现的因素已成为一个对我们社会有重大影响的非常重要的研究课题。虽然过去的研究试图确定表现的预测因素，但它们的预测能力并不令人满意。因此，在这项初步研究中，我们试图根据使用脑电图（EEG）记录的人脑信号，预测在不同室内温度（22.2摄氏度和30摄氏度）触发的办公任务期间的表现。招募了七名参与者，收集了他们的脑电图、皮肤温度、心率和热调查问卷。进行了回归分析，以研究使用脑电图功率谱密度（PSD）作为表现预测指标的有效性。我们的结果表明，以脑电图PSD作为预测指标时，其相关性最高（>0.70），比使用其他生理信号作为预测指标高17倍，且更为稳健。最后，本文根据不同室内温度下低、高性能水平的大脑活动模式，对所选预测指标提供了见解。
