Design of human factor evaluations system based on EEG and eye-tracking

Abstract

Human factors are often associated with reducing error, enhancing safety, enhancing comfort and increasing productivity. Poor evaluation of human factors in working environments often result in human error which leads to the occurrence of critical accidents. In order to create an efficient, effective and safe working environment for workers and the development of appropriate prevention and mitigation strategies, studies conducted on mental workload and the influence it has on human error has been increasing over the years. To allow for easy and fast analysis and detection of workload in working individuals, a system was proposed to assist human factor specialists in determining mental workload throughout a task. The system used raw data collected from EEG and Eye-tracking devices and processed the data through machine-learning algorithms. An accuracy of probability and the mental workload results were displayed through graphs of mental workload against time (seconds). The system allowed users to analyse mental workload results from different sources and compared them for better analysis. It was concluded that the system could be further enhanced with additional features to provide a more reliable and accurate result of mental workload.