BCI-Based Navigation in Virtual and Real Environments

A Brain-Computer Interface (BCI) is a system that enables people to control an external device with their brain activity, without the need of any muscular activity. Researchers in the BCI field aim to develop applications to improve the quality of life of severely disabled patients, for whom a BCI can be a useful channel for interaction with their environment. Some of these systems are intended to control a mobile device (e. g. a wheelchair). Virtual Reality is a powerful tool that can provide the subjects with an opportunity to train and to test different applications in a safe environment. This technical review will focus on systems aimed at navigation, both in virtual and real environments.This work was partially supported by the Innovation, Science and Enterprise Council of the Junta de Andalucía (Spain), project P07-TIC-03310, the Spanish Ministry of Science and Innovation, project TEC 2011-26395 and by the European fund ERDF

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Review of real brain-controlled wheelchairs

This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface (BCI). Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic (EEG) signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future

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 is able to 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, making the system safer and more reliable. In this regard, the robot can exploit two maps of the environment, one for localization and one for navigation, and both can be used also by the BCI user to watch the position of the robot while it is moving. As demonstrated by the experimental results, the user's cognitive workload is reduced, decreasing the number of commands necessary to complete the task and helping him/her to keep attention for longer periods of time.Comment: Accepted in the Proceedings of the 2018 IEEE International Conference on Robotics and Automatio

The Application of Neurologically-Controlled Robotics to Actuated Feeding Arms

The medical industry is constantly performing research and trying to combat various diseases that afflict humans. Despite advancements in technology, there still remain diseases that have no cure but seem prime candidates for neurologically controlled robots. One such category of diseases consists of various muscular dystrophic diseases. Diseases such as ALS and Parkinson’s have limited options regarding treatment, but by brain controlled interfaces (BCI’s), robotics can help mitigate the impact on a patient’s quality of life. By utilizing a functioning mind, an electroencephalographic (EEG) helmet can be used to control various exoskeletal systems and even prosthesis in order to compensate for a damaged motor system. Through the use of neurologically controlled robotics, a user’s motor control and motor strength can be rehabilitated and maintained despite the effects of muscular dystrophic diseases. The goal of this project is to use this experiment to demonstrate the current effectiveness of brain actuated robotics and telepresence that utilize an EEG Sensor. From this assessment, recommendations and further improvements can be made to this existing technology for it to be better suited for electrical engineering and biomedical applications, while simultaneously taking the technology into a new realm of application

Non-invasive Brain actuated control of a mobile robot by Human EEG

Brain activity recorded non-invasively is sufficient to control a moblie robot if advanced robotics is used in combination with asynchronous EEG analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG based systems have bben considered tto slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only using an EEG based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a preformance ration of 0.74