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 i mportance of task performance of certain jobs requiring mental functio ns 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. Consequentl y, productivity measurement and improvement for white collar or 'knowl edge worker' occupations remains a major challenge and concern. This i nvestigation defines an experimental approach that examines the neurop hysiological correlates of white collar worker mental fatigue using th e EEG signal. A 6 h laboratory experiment was conducted to simulate wo rk output. The methods of assessing fatigue employed were mental tests and physiological measurements. The experiment involved reading of st andardized texts, finding solutions to arithmetic-logical problems and a combination of both task types. Two primary performance measures we re obtained, work output and brain waves. Fast Fourier transform and c orrelation analyses are used to quantify the relationship between cert ain brain waves and mental fatigue. This research is a major step towa rds 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 use ful in human reliability prediction based on task parameters and worke r profiles.