On the investigation of the neurophysiological correlates of knowledge worker mental fatigue using the EEG signal.

Technological trends and advances in automation have underscored the importance of task performance of certain jobs requiring mental functions such as information processing and decision analyses. Most experts agree that such work environments produce increased mental activities, with profound implications for mental fatigue and stress. Consequently, productivity measurement and improvement for white collar or 'knowledge worker' occupations remains a major challenge and concern. This investigation defines an experimental approach that examines the neurophysiological correlates of white collar worker mental fatigue using the EEG signal. A 6 h laboratory experiment was conducted to simulate work output. The methods of assessing fatigue employed were mental tests and physiological measurements. The experiment involved reading of standardized texts, finding solutions to arithmetic-logical problems and a combination of both task types. Two primary performance measures were obtained, work output and brain waves. Fast Fourier transform and correlation analyses are used to quantify the relationship between certain brain waves and mental fatigue. This research is a major step towards the development of a model that explores the relationship between mental fatigue and factors associated with output performance, optimal recuperation periods and related variables. Such a model would be useful in human reliability prediction based on task parameters and worker profiles.