Analyzing the impact of Driving tasks when detecting emotions through brain–computer interfaces

Abstract Traffic accidents are the leading cause of death among young people, a problem that today costs an enormous number victims. Several technologies have been proposed to prevent accidents, being brain–computer interfaces (BCIs) one most promising. In this context, BCIs used detect emotional states, concentration issues, or stressful situations, which could play fundamental role in road since they directly related drivers’ decisions. However, there is no extensive literature applying subjects’ emotions driving scenarios. such some challenges be solved, as (i) impact performing task on emotion detection and (ii) more detectable To improve these challenges, work proposes framework focused detecting using electroencephalography with machine learning deep algorithms. addition, use case has designed where two scenarios presented. The first scenario consists listening sounds primary perform, while second sound becomes secondary task, simulator. way, it intended demonstrate whether useful scenario. results those existing literature, achieving 99% accuracy for (non-stimuli angry), 93% three (non-stimuli, angry neutral) 75% four angry, neutral joy).