Prediction of Human Performance Using Electroencephalography under Different Indoor Room Temperatures

Area of Science:

• Neuroscience
• Environmental Psychology
• Occupational Health

Background:

• Indoor environmental conditions significantly impact office worker performance.
• Previous studies linked poor temperature and air quality to sick building syndrome (SBS).
• Existing performance prediction models lack satisfactory accuracy.

Purpose of the Study:

• To predict office worker performance using electroencephalography (EEG) signals.
• To investigate the impact of different indoor temperatures (22.2°C and 30°C) on performance.
• To identify reliable predictors of performance in dynamic indoor environments.

Main Methods:

• Collected EEG, skin temperature, heart rate, and thermal comfort data from seven participants.
• Utilized regression analysis to assess EEG power spectral densities (PSD) as performance predictors.
• Compared EEG PSDs against other physiological signals for predictive accuracy.

Main Results:

• EEG PSDs demonstrated high predictive power for performance (R² > 0.70).
• EEG-based predictions were 17 times more accurate than those from other physiological signals.
• EEG PSDs proved to be a robust predictor across different temperature conditions.

Conclusions:

• EEG signals, specifically PSDs, are highly effective predictors of office worker performance.
• This study highlights the potential of neurophysiological monitoring for optimizing indoor work environments.
• Understanding brain activity patterns can inform strategies for maintaining high performance levels regardless of temperature fluctuations.