Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

Tapsya Nayak,Tinghe Zhang,Yufei Huang,Bing Dong,Xiaojing Xu,Daniel Pack,Zijing Mao,Lin Zhang

doi:10.3390/brainsci8040074

Abstract

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 R2 (> 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.

Highlights

As U.S citizens spend more than 90% of their time indoors, indoor thermal condition is a key factor that impacts human productivity in the office [1,2,3,4,5]
The goal of this paper is to assess the efficiency of using EEG signals in performance prediction induced by varying indoor room temperatures
This paper is organized into three parts: first, we present statistical results to validate performance is effected by indoor temperatures; second, we show the prediction results of office-work performance using features reported by past research—thermal sensation, thermal comfort, skin temperature and heart rate in a linear regression model; and third, we present the prediction ability and robustness of using EEG power spectral densities (PSD) as predictors in a linear regression model enhanced with least absolute shrinkage and selection operator (LASSO)

Summary

Introduction

As U.S citizens spend more than 90% of their time indoors, indoor thermal condition is a key factor that impacts human productivity in the office [1,2,3,4,5]. Indoor environments and building characteristics have been reported to impact occurrences of respiratory diseases, allergy and asthma symptoms, sick building symptoms and office-work performance. Brain Sci. 2018, 8, 74 changing indoor environments have become highly important research topics that bear significant economic and sociological impact. As our indoor daily work becomes increasingly mentally challenging, a significant aspect of the thermal-driven performance is an individual’s cognitive performance, that is, the ability of an individual to effectively comprehend and perform independent decisions during complex tasks and events. Various field and laboratory studies have been conducted to investigate performance levels and changes under different thermal conditions. A study investigated in Reference [7] showed an

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