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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, would(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)