Personality Assessment Based on Electroencephalography Signals during Hazard Recognition

Hazard recognition assisted by human–machine collaboration (HMC) techniques can facilitate high productivity. Human–machine promote safer working processes reducing the interaction between humans and machines. Nevertheless, current HMC acquire human characteristics through manual inputs to provide customized information, thereby increasing need for an interactive interface. Herein, we propose implicit electroencephalography (EEG)-based measurement system automatically assess worker personalities, underpinning development of techniques. Assuming that personality influences hazard recognition, recorded signals construction workers subsequently proposed a supervised machine-learning algorithm extract multichannel event-related potentials develop model assessment. The analyses showed (1) electroencephalography-assessed results had strong correlation with self-reported results; (2) achieved good external validity recognition-related out-of-sample reliability; (3) stronger engagement levels correlations task performance than work experience. Theoretically, this study demonstrates feasibility assessing using during recognition. In practice, assessment parametric basis intelligent devices in collaboration.