Defining brain–machine interface applications by matching interface performance with device requirements

Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications. © 2007 Elsevier B.V. All rights reserved

Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots

This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore. Thus, the BCI user can actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot. To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance in order to make the system safer and more reliable. In this regard, the robot exploits two maps of the environment, one for localization and one for navigation, and both are used as additional visual feedback for the BCI user to control the robot position. Experimental results show a decrease of the number of commands needed to complete the navigation task, suggesting a reduction user’s cognitive workload. The novelty of this work is to provide a first evidence of an integration between BCI and ROS that can simplify and foster the development of software for BCI driven robotics devices

The future of laboratory medicine - A 2014 perspective.

Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine

In-home and remote use of robotic body surrogates by people with profound motor deficits

By controlling robots comparable to the human body, people with profound motor deficits could potentially perform a variety of physical tasks for themselves, improving their quality of life. The extent to which this is achievable has been unclear due to the lack of suitable interfaces by which to control robotic body surrogates and a dearth of studies involving substantial numbers of people with profound motor deficits. We developed a novel, web-based augmented reality interface that enables people with profound motor deficits to remotely control a PR2 mobile manipulator from Willow Garage, which is a human-scale, wheeled robot with two arms. We then conducted two studies to investigate the use of robotic body surrogates. In the first study, 15 novice users with profound motor deficits from across the United States controlled a PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a simulated self-care task. Participants achieved clinically meaningful improvements on the ARAT and 12 of 15 participants (80%) successfully completed the simulated self-care task. Participants agreed that the robotic system was easy to use, was useful, and would provide a meaningful improvement in their lives. In the second study, one expert user with profound motor deficits had free use of a PR2 in his home for seven days. He performed a variety of self-care and household tasks, and also used the robot in novel ways. Taking both studies together, our results suggest that people with profound motor deficits can improve their quality of life using robotic body surrogates, and that they can gain benefit with only low-level robot autonomy and without invasive interfaces. However, methods to reduce the rate of errors and increase operational speed merit further investigation.Comment: 43 Pages, 13 Figure

On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world