neurological measurement of human trust in automation using electroencephalogram

In modern society, automation is complex enough to perform advanced tasks. the role of the human operator to control and monitor complex automations is crucial to avoid failure, to reduce risk, and to prevent unpredictable situations. measuring the human operators’ level of trust is crucial in predicting their acceptance and reliance on the automation. this research used an electroencephalogram (eeg) as a neurological measure to identify specific brainwaves in situations of trust and mistrust in automation. power spectrum analysis was used for brainwave analysis. the results indicate that the power of alpha and beta waves was stronger for the trust situation; whereas, the power of gamma waves was stronger for the mistrust situation. when the level of human trust in automation increases, the use of an automatic control increases. therefore, the findings of this research will contribute to defining how trust in automation affects the human operator’s monitoring, decision-making, and overall performance.