Predicting Driver Stress Levels with a Sensor-Equipped Steering Wheel and a Quality-Aware Heart Rate Measurement Algorithm.

Unobtrusive monitoring of driver mental states has been regarded as an important element in improving the safety of existing transportation systems. While many solutions exist relying on camera-based systems for e.g.

Gymkhana and pylon slalom driving training effects on the cerebellum structure.

To develop a suitable automobile design as per each driver's characteristics and state, it is important to understand the brain function in acquiring driving skills. Reportedly, the brain structures of professionals, such as athletes and musicians, and those who have received training in special skills, undergo changes with training. However, the development process of the brain in terms of acquiring driving skills has not yet been clarified.

Action prediction based on anticipatory brain potentials during simulated driving.

Objective: The ability of an automobile to infer the driver's upcoming actions directly from neural signals could enrich the interaction of the car with its driver. Intelligent vehicles fitted with an on-board brain-computer interface able to decode the driver's intentions can use this information to improve the driving experience. In this study we investigate the neural signatures of anticipation of specific actions, namely braking and accelerating.