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

Training in realistic virtual environments: Impact on user performance in a motor imagery-based Brain-Computer-Interface

A brain–computer interface (BCI) is a system that enables people to control an external device by means of their brain activity, without the need of performing muscular activity. BCI systems are normally first tested on a controlled environment before being used in a real, daily scenario. While this is due to security reasons, the conditions that BCI systems users will eventually face in their usual environment may affect their performance in an unforeseen way. In this paper, we try to bridge this gap by presenting a trained BCI user a virtual environment that includes realistic distracting stimuli and testing whether the complexity or the type of such stimuli affects user performance. 11 subjects navigated two virtual environments: a static park and the same one with visual and auditory stimuli simulating typical distractors from a real park. No significant differences were found when using a realistic environment; in other words, the presence of different distracting stimuli did not worsen user performance.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy

<p>Abstract</p> <p>Background</p> <p>For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home.</p> <p>Results</p> <p>After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (<it>p </it>< 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%).</p> <p>Conclusions</p> <p>These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.</p

Brain-Controlled Wheelchair Through Discrimination of Two Mental Tasks

Recently, Brain-Computer Interface (BCI) research has been targeted at the rehabilitation of motor-disabled individuals because it helps to establish a communication and control channel for them. This new channel could be used to restore motor functions or to provide them with mobility using a BCI controlled motorized wheelchair. One of the most important limitations of these systems is to guarantee that a person can, through his mental activity, safely control the variety of navigation commands that provide control of the wheelchair: advance, turn, move back, and stop. The vast majority of the mobile robot navigation applications that are controlled via a BCI demand that the user performs as many different mental tasks as there are different control commands. Having a higher number of commands makes it easier for the subjects to navigate through the environment, since they have more choices to move. However, despite this is an intuitive solution, the classification accuracy of such systems gets worse as the number of mental tasks to identify increases. Some studies proved that the best classification accuracy is achieved when only two classes are discriminated. In order to enable an effective and autonomous wheelchair navigation with a BCI system without worsening user performance, our group proposed and later developed a new paradigm based on the discrimination of only two classes (one active mental task versus any other mental activity), which enabled the selection of four commands, besides the stop command: move forwards, turn right, move backward and turn left. In the present study, a subject participated in an experiment in order to freely control a wheelchair carrying out continuous movements. The obtained results suggest that the proposed BCI system seems to be an effective way of driving a robotic wheelchair autonomously.This work was partially supported by the University of Málaga, by the Spanish Ministry of Economy and Competitiveness through the projects LICOM (DPI2015-67064-R) and INCADI (TEC 2011-26395), and by the European Regional Development Fund (ERDF).2018-12-3