Real-time mental workload measurement for four break configurations in cognitive tasks using the EEG-based workload index (EWI)

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Abstract The current real tasks in work environments are complex, they demand a large amount of cognitive work, which causes the need to carry out Mental Workload (MWL) studies during the execution of those tasks. In high-tech manufacturing processes, the human-machine systems adopted are technologically more complex than ever and, consequently, the MWL of employees has increased during their work activities. A high MWL can adversely affect employee well-being, causing new health problems, mainly cognitive. MWL has become a relevant workplace concern due to its relationship with poor performance, low productivity, human errors, accidents, and stress. In this project, a design of experiments framework was created to measure MWL through a graphical user interface (GUI) that contains the Dual N-back method, it is developed by subjects while they performed a set of cognitive tasks; and it is measured physiologically by electroencephalogram (EEG). The design integrated hardware (Cyton© card as EEG measurement equipment) and software (OpenBCI, MATLAB, EEGLAB) that automatically synchronizes the electroencephalogram (EEG) with the activity performed (stimulus), this was done by using the Lab Streaming Layer (LSL)