Neuroscience-based or neuroscience-informed design takes its roots in the study of human well-being in architecture and human factor study in engineering and manufacturing.
The EEG is used as a tool to monitor and record the brain states of subjects during human factors study experiments. In traditional human factors studies, the data of mental workload, stress, emotion and vigilance recognition are obtained through questionnaires that are administered upon completion of some task or the whole experiment. However, this method only offers the evaluation of overall feelings subjects during the task performance and/or after the experiment.
Real-time EEG-based human factors evaluation of systems allows researchers to analyse the changes of subjects’ brain states during the performance of various tasks. Machine learning techniques are applied to the EEG data to recognize levels of mental workload, stress, emotion and vigilance during each task.
By utilizing the proposed EEG-based system, true understanding of subjects working pattern can be obtained. Based on the analyses of the objective real time data together with the subjective feedback from the subjects, we are able to reliably evaluate current systems and working place design, and refine new concepts of future systems.