Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a Brain-Computer Interface (BCI). However, to facilitate the interaction aspect that underpins the notion of telepresence, users must be able to voluntarily and reliably stop the robot at any moment, not just drive from point to point. In this work, we propose to exploit the user’s residual muscular activity to provide a fast and reliable control channel, which can start/stop the telepresence robot at any moment. Our preliminary results show that not only does this hybrid approach increase the accuracy, but it also helps to reduce the workload and was the preferred control paradigm of all the participants.
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Our brain-actuated wheelchair uses shared control to couple the user input with the contextual information about the surroundings in order to perform natural manoeuvres both safely and efficiently. In this study, we investigate the feasibility of using our brain–controlled wheelchair with patients in a rehabilitation clinic. Both user and system performance metrics are analysed. We find that the driving performance of a motor-disabled patient at the clinic is comparable with the performance of four healthy subjects. All five participants were able to complete the driving task successfully.
For people with severe physical disabilities, low resolution input devices, such as buttons, sip and puff switches and brain–computer interfaces provide an opportunity to interact with the world. However, it can be difficult to control assistive technology, such as wheelchairs, tele–presence robots and robotic arms, when you have only a limited number of commands available and/or a lack of temporal precision in issuing such commands. These limitations can be overcome by employing shared control techniques, whereby the system assists the user in performing the desired task. In this study we compare the use of a simple discrete shared control policy with a more dynamic proportional shared control policy. We evaluate both approaches on a wheelchair that is only operated by two temporally– constrained discrete buttons. The experiments were performed in two different realistic indoor scenarios: an open–plan, spacious environment and a smaller, more cluttered ofﬁce environment. A total of 10 healthy participants took part in this study.
In order for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the users evolving needs. This adaptation should not only be a function of the environmental (external) context, but should also consider the internal context, such as cognitive states and brain signal reliability. In this work, we propose three different shared control frameworks that have been used for BCI applications: contextual fusion, contextual gating, and contextual regulation. We review recently published results in the light of these three context-awareness frameworks. Then, we discuss important issues to consider when designing a shared controller for BCI.
In this paper we argue that for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the user’s evolving needs. This adaptation should not only be a function of the environmental (external) context, but should also consider the internal context, such as cognitive states and brain signal reliability. We demonstrate two successful approaches to modulating the level of assistance: by using online task performance metrics; and by monitoring the reliability of the BCI decoders. We then describe how these approaches could be fused together, resulting in a more user-centred solution.
In this paper we propose a method to modulate the level of assistance provided by a shared controller, not only given the environmental context, but also according to the context of the user’s current behaviour. We show that the enhanced situational context can be adequately captured by using online performance metrics (such as those more usually found in the evaluation of shared control systems). The resultant controller not only allows the user to perform better in the primary task (like many shared control systems), but has also has increased the level of user acceptance, due to the personalised dynamics of the control policy.
Providing adaptive shared control for Brain- Computer Interfaces (BCIs) can result in better performance while reducing the user’s mental workload. In this respect, online estimation of accuracy and speed of command delivery are important factors. This study aims at real-time differentiation between fast and slow trials in a motor imagery BCI. In our experiments, we refer to trials shorter than the median of trial lengths as “fast” trials and to those longer than the median as “slow” trials. We propose a classifier for real-time distinction between fast and slow trials based on estimates of the entropy rates for the first 2-3 s of the electroencephalogram (EEG). Results suggest that it can be predicted whether a trial is slow or fast well before a cutoff time. This is important for adaptive shared control especially because 55% to 75% of trials (for the five subjects in this study) are longer than that cutoff time
This paper presents an important step forward towards increasing the independence of people with severe motor disabilities, by using brain-computer interfaces (BCI) to harness the power of the Internet of Things. We analyze the stability of brain signals as end-users with motor disabilities progress from performing simple standard on-screen training tasks to interacting with real devices in the real world. Furthermore, we demonstrate how the concept of shared control —which interprets the user’s commands in context— empowers users to perform rather complex tasks without a high workload. We present the results of nine end-users with motor disabilities who were able to complete navigation tasks with a telepresence robot successfully in a remote environment (in some cases in a different country) that they had never previously visited. Moreover, these end-users achieved similar levels of performance to a control group of ten healthy users who were already familiar with the environment.