Author:

Keywords:

Science & Technology, Social Sciences, Technology, Engineering, Industrial, Ergonomics, Psychology, Applied, Engineering, Psychology, Industrial setting, Assembly task, Cognitive workload, Cognitive ergonomics, Overload, EEG, EOG, MENTAL WORKLOAD, WORKING-MEMORY, WIRELESS EEG, PERFORMANCE, TASK, ALPHA, LOAD, OSCILLATIONS, OPERATORS, INDEXES, Artificial Intelligence, Cognition, Electroencephalography, Electrooculography, Eye Movements, Humans, 1106 Human Movement and Sports Sciences, 1116 Medical Physiology, 1203 Design Practice and Management, Human Factors, 3505 Human resources and industrial relations, 4201 Allied health and rehabilitation science, 5201 Applied and developmental psychology

Abstract:

Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase cognitive workload, or potentially induce overload. Measurement and optimization protocols need to be developed in order to be able to monitor workers’ cognitive load. Previous studies have used electroencephalographic (EEG, measuring brain activity) and electrooculographic (EOG, measuring eye movements) signals, using basic computer-based static tasks and without creating an experience of overload. In this study, EEG and EOG data was collected of 46 participants performing an ecologically valid assembly task while inducing three levels of cognitive load (low, high and overload). The lower individual alpha frequency (IAF) was identified as a promising marker for discriminating between different levels of cognitive load and overload.