EEG-Based Mental Workload and Stress Monitoring of Crew Members in Maritime Virtual Simulator

摘要: Many studies have shown that most maritime accidents are attributed to human error as the initiating cause, resulting in a need for study of factors improve safety transportation. Among various techniques, Electroencephalography (EEG) has key advantage high time resolution, with possibility continuously monitor brain states including mental workload, emotions, stress levels, etc. In this paper, we proposed novel workload recognition algorithm using deep learning techniques outperformed state-of art algorithms and successfully applied it crew members’ simulator. We designed carried out an experiment collect EEG data, which was used distribution among members during collaborative tasks ship’s bridge The consisted two parts. part 1, 3 trainees fulfilled without experienced captain. results analyses showed 2 had less when captain present. 2, 4 collaborated each other Our findings trainee who acted highest levels while three low due shared work responsibility. These suggest is promising evaluation tool applicable domain.