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OBJECTIVE To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. METHODS In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only(More)
— The use of shared control techniques has a profound impact on the performance of a robotic assistant controlled by human brain signals. However, this shared control usually provides assistance to the user in a constant and identical manner each time. Creating an adaptive level of assistance, thereby complementing the user's capabilities at any moment,(More)
In this work we present a novel system for autonomous mobile robot navigation. With only an omnidi-rectional camera as sensor, this system is able to build automatically and robustly accurate topologically organised environment maps of a complex, natural environment. It can localise itself using such a map at each moment, including both at startup(More)
Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper(More)
— Vision sensors are attractive for autonomous robots because they are a rich source of environment information. The main challenge in using images for mobile robots is managing this wealth of information. A relatively recent approach is the use of fast wide baseline local features, which we developed and used in the novel approach to sparse visual path(More)
SUMMARY: Feedback plays an important role when learning to use a BCI. Here we compare visual and haptic feedback in a short experiment. By imagining left and right hand movements, six novice subjects tried to control a BCI with the help of either visual or haptic feedback every 1s. Alpha band EEG signals from C3 and C4 were classified. The classifier was(More)
This article proposes a software framework for multi-sensor, multi-actuator systems, such as our mobile robot LiAS (Leuven intelligent Autonomous System , see figure 1). The framework is based on an agent-based philosophy, which makes it extremely useful for programming behaviour-based controllers, but it is applicable for any type of control. The framework(More)