Brice Rebsamen

A Brain Controlled Wheelchair to Navigate in Familiar Environments

While brain-computer interfaces (BCIs) can provide communication to people who are locked-in, they suffer from a very low information transfer rate. Further, using a BCI requires a concentration effort and using it continuously can be tiring. The brain controlled wheelchair (BCW) described in this paper aims at providing mobility to BCI users despite these limitations, in a safe and efficient way. Using a slow but reliable P300 based BCI, the user selects a destination amongst a list of predefined locations. While the wheelchair moves on virtual guiding paths ensuring smooth, safe, and predictable trajectories, the user can stop the wheelchair by using a faster BCI. Experiments with nondisabled subjects demonstrated the efficiency of this strategy. Brain control was not affected when the wheelchair was in motion, and the BCW enabled the users to move to various locations in less time and with significantly less control effort than other control strategies proposed in the literature.

A Brain-Controlled Wheelchair Based on P300 and Path Guidance

This paper presents the first working prototype of a brain controlled wheelchair able to navigate inside a typical office or hospital environment. This brain controlled wheelchair (BCW) is based on a slow but safe P300 interface. To circumvent the problem caused by the low information rate of the EEG signal, we propose a motion guidance strategy providing safe and efficient control without complex sensors or sensor processing. Experiments demonstrated that healthy subjects could safely control the wheelchair in an office like environment, without any training

A Collaborative Wheelchair System

This paper describes a novel robotic wheelchair, and reports experiments to evaluate its efficiency and understand how human operators use it. The concept at the heart of the collaborative wheelchair assistant (CWA) is to rely on the users motion planning skills while assisting the maneuvering with flexible path guidance. The user decides where to go and controls the speed (including start and stop), while the system guides the wheelchair along software-defined guide paths. An intuitive path editor allows the user to avoid dangers or obstacles online and to modify the guide paths at will. By using the human sensory and planning systems, no complex sensor processing or artificial decision system is needed, making the system safe, simple, and low-cost. We investigated the performance of the CWA on its interaction with able-bodied subjects and motion efficiency. The results show that path guidance drastically simplifies the control. Using the CWA, the wheelchair user needs little effort from the first trial, while moving efficiently with a conventional wheelchair requires adaptation.

Controlling a wheelchair using a BCI with low information transfer rate

This paper describes a control hierarchy to drive a wheelchair using an interface with asynchronous and very low information transfer rate signal. Path guiding assistance allows the user to bring his or her wheelchair in a building environment, from one destination to the next destination. The user can stop the wheelchair voluntarily during movement, or through a reflex elicited by sensors. Decisions are simplified by presenting only the possible selections on the GUI, in a context dependent menu. This system is implemented on a conventional wheelchair with a P300 Brain Machine Interface. Tests with healthy subjects show that this system can move the wheelchair in a typical building environment according to the wishes of its user, and that the brain control is not disturbed by the movement.

Evaluation of the Collaborative Wheelchair Assistant System

The collaborative wheelchair assistant (CWA) is a robotic wheelchair which makes full use of human skills, by involving the user into the navigation control. The user gives the high-level commands and directly controls the speed, while the low-level control is taken over by the machine, which is tracking a software defined guide path. This paper presents an evaluation of the CWA system, consisting of experiments performed with human subjects. We investigated the performance of the system in terms of its interaction with healthy subjects and motion efficiency. Initial experiments with a cerebral palsy subject were also performed. The results show that path guidance brings safe motion and drastically simplifies the control. The wheelchair user adopts a driving behavior which is optimal from the first trial and requires little intervention.

Design of a collaborative wheelchair with path guidance assistance

This paper describes the development and assessment of a collaborative wheelchair assistant (CWA). The concept at the heart of the CWA is to rely on the users motion planning skills and assist the maneuvering with path guidance. The user decides where to go and controls the speed (including start and stop), while the system guides the wheelchair along software-defined paths. An intuitive path editor allows the user to modify the path on-line and so avoid dangers or obstacles. By using the human sensory and planning systems, complex sensor processing and artificial decision systems are not needed, making the system safe, simple and low-cost. Experimental results demonstrate this concept

Evaluation Of Cognitive Workload From EEG During A Mental Arithmetic Task

We collected electroencephalographic (EEG) data from 16 subjects while they performed a mental arithmetic task at five different levels of difficulty. A classifier was trained to discriminate between three conditions: relaxed, low workload and high workload, using spectral features of the EEG. We obtained an average classification accuracy of 62%. A continuous workload index was obtained by low-pass filtering the classifier’s output. The correlation coefficient between the resulting workload index and the difficulty level of the task was 0.6 on average.

Real-Time Wireless Sonification of Brain Signals

In this paper, an alternative representation of EEG is investigated, in particular, translation of EEG into sound; patterns in the EEG then correspond to sequences of notes. The aim is to provide an alternative tool for analysing and exploring brain signals, e.g., for diagnosis of neurological diseases. Specifically, a system is proposed that transforms EEG signals, recorded by a wireless headset, into sounds in real-time. In order to assess the resulting representation of EEG as sounds, the proposed sonification system is applied to EEG signals of Alzheimer’s (AD) patients and healthy age-matched control subjects (recorded by a high-quality wired EEG system). Fifteen volunteers were asked to classify the sounds generated from the EEG of 5 AD patients and five healthy subjects; the volunteers labeled most sounds correctly, in particular, an overall sensitivity and specificity of 93.3% and 97.3% respectively was obtained, suggesting that the sound sequences generated by the sonification system contain relevant information about EEG signals and underlying brain activity.

Collaborative path planning for a robotic wheelchair.

Generating a path to guide a wheelchairs motion faces two challenges. First, the path is located in the human environment and that is usually unstructured and dynamic. Thus, it is difficult to generate a reliable map and plan paths on it by artificial intelligence. Second, the wheelchair, whose task is to carry a human user, should move on a smooth and comfortable path adapted to the users intentions. To meet these challenges, we propose that the human operator and the robot interact to create and gradually improve a guide path. This paper introduces design tools to enable an intuitive interaction, and reports experiments performed with healthy subjects in order to investigate this collaborative path learning strategy. We analyzed features of the optimal paths and user evaluation in representative conditions. This was complemented by a questionnaire filled out by the subjects after the experiments. The results demonstrate the effectiveness of this approach, and show the utility and complementarity of the tools to design ergonomic guide paths.

Experiments on collaborative learning with a robotic wheelchair

To generate a path that guides the wheelchairs motion faces several challenges: The path is located in the human environment, which is usually unstructured and dynamic, and thus is difficult or impossible to generate a reliable map and plan paths on it by artificial intelligence. In addition, the path of a wheelchair, whose task is to carry the human user, should be smooth and comfortable, and adapted to the users intentions, which may evolve with time. We propose a collaborative learning strategy corresponding to these requirements, according to which the human operator and the robot, using the provided path design tools, create and gradually improve a guide path, eventually resulting in ergonomic motion guidance. This paper reports experiments performed to investigate this collaborative learning strategy. To evaluate the path design tools, we analyzed features of the optimal paths and user evaluation in representative conditions. This was complemented by a questionnaire filled out by the subjects after the experiments. The results demonstrate the effectiveness of the collaborative learning strategy, and show the utility and complementarity of the path design tools.