Brain–computer interface for autonomous vehicles: An investigation of a hands-free control system
Examensarbete för masterexamen
Brain–computer interfaces have been a subject of growing interest in recent years and devices measuring brainwave activity are being used within vehicles for evaluating attentiveness and detecting steering intentions. Implementation of such an interface for vehicle control is the main focus of this thesis. In this work, a novel EEG-based interface supported by eye tracking is investigated in a vehicle control application. The proposed solution merges algorithms utilized in autonomous driving systems with a brain–computer interface based on a P300 response. The control method introduces target following rather than directional steering as a principle of BCI driving, potentially simplifying control and reducing the influence of delay typical for electroencephalography classification. The interface has been tested by five untrained participants in a simulated laboratory environment. The testing platform consisted of an OpenBCI EEG headset, Pupil Labs wearable eye tracker connected to a standard PC unit, and a miniature robot platform equipped to semi-autonomously maneuver, follow and avoid objects which served as a controlled vehicle. The participants were asked to perform simple driving tasks by observing the frontal camera feed on the computer monitor while their brain response was being recorded and a signal pattern acted as a trigger. Target marking was realized by tracking the gaze position in the character of a selector, and a brain response matching a theoretical P300 was interpreted as a will to interact with the object. The subjects were interacting with the interface intuitively and were generally able to complete the tasks. The hardships arose in relation to the measuring equipment, which was revealed to be of unsatisfactory quality. While the results are very promising and point to the proposed target-based steering as preferable for BCI driving, further work is recommended to fully estimate the applicability in a real world scenario.
brain , computer , interface , EEG , P300 , autonomous , driving , BCI