Non-Invasive Brain-Actuated Interaction

The promise of Brain-Computer Interfaces (BCI) technology is to augment human capabilities by enabling interaction with computers through a conscious and spontaneous modulation of the brainwaves after a short training period. Indeed, by analyzing brain electrical activity online, several groups have designed brain-actuated devices that provide alternative channels for communication, entertainment and control. Thus, a person can write messages using a virtual keyboard on a computer screen and also browse the internet. Alternatively, subjects can operate simple computer games, or brain games, and interact with educational software. Work with humans has shown that it is possible for them to move a cursor and even to drive a wheelchair. This paper briefly reviews the field of BCI, with a focus on non-invasive systems based on electroencephalogram (EEG) signals. It also describes three brain-actuated devices we have developed: a virtual keyboard, a brain game, and a wheelchair. Finally, it shortly discusses current research directions we are pursuing in order to improve the performance and robustness of our BCI system, especially for real-time control of brain-actuated robots

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

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

Adaptive classification of mental states for asynchronous brain computer interfaces

ISBN : 978-2-9532965-0-1Brain Computers Interfaces (BCI) are emerging as a new communicational device, aiming to make a direct link between the brain and an external device, bypassing conventional motor outputs, such as peripheral nerves and muscles. A BCI extracts features from a brain signal and classifies them in order to interpret them in terms of the user's volition. For communication to be effective, the computer has to provide feedback to the user allowing him/her to judge how the brain activity is being classified and interpreted. Similarly, the user must produce patterns of brain activity which can easily be learned and recognized by the computer. Here, we describe a method for selecting mental tasks that are best classified by a subject using support vector machines (SVM)

Towards a Cure for BCI Illiteracy

Brain–Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research is to understand and solve the problem of “BCI Illiteracy”, which is that BCI control does not work for a non-negligible portion of users (estimated 15 to 30%). Here, we investigate the illiteracy problem in BCI systems which are based on the modulation of sensorimotor rhythms. In this paper, a sophisticated adaptation scheme is presented which guides the user from an initial subject-independent classifier that operates on simple features to a subject-optimized state-of-the-art classifier within one session while the user interacts the whole time with the same feedback application. While initial runs use supervised adaptation methods for robust co-adaptive learning of user and machine, final runs use unsupervised adaptation and therefore provide an unbiased measure of BCI performance. Using this approach, which does not involve any offline calibration measurement, good performance was obtained by good BCI participants (also one novice) after 3–6 min of adaptation. More importantly, the use of machine learning techniques allowed users who were unable to achieve successful feedback before to gain significant control over the BCI system. In particular, one participant had no peak of the sensory motor idle rhythm in the beginning of the experiment, but could develop such peak during the course of the session (and use voluntary modulation of its amplitude to control the feedback application)

Control de una silla de ruedas a través de un sistema BCI basado en la discriminación de dos tareas mentales

Dicho trabajo ha sido publicado en la revista Cognitive Area Networks, vol. 1, nº1, junio 2014, pp. 43-48.En este trabajo se presenta una propuesta de control de una silla de ruedas a través de una interfaz cerebro computadora mediante la discriminación de sólo dos tareas mentales. Con el paradigma de control propuesto se pretende minimizar los porcentajes de error en la clasificación de estados mentales, proporcionando a su vez un conjunto suficiente de comandos que permita dirigir la silla de ruedas en todas direcciones (avanzar, retroceder, girar a la derecha, girar a la izquierda y parar).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Prospects of brain–machine interfaces for space system control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain-machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation. © 2008 Elsevier Ltd. All rights reserved

BNCI Horizon 2020 - Towards a Roadmap for Brain/Neural Computer Interaction

In this paper, we present BNCI Horizon 2020, an EU Coordination and Support Action (CSA) that will provide a roadmap for brain-computer interaction research for the next years, starting in 2013, and aiming at research efforts until 2020 and beyond. The project is a successor of the earlier EU-funded Future BNCI CSA that started in 2010 and produced a roadmap for a shorter time period. We present how we, a consortium of the main European BCI research groups as well as companies and end user representatives, expect to tackle the problem of designing a roadmap for BCI research. In this paper, we define the field with its recent developments, in particular by considering publications and EU-funded research projects, and we discuss how we plan to involve research groups, companies, and user groups in our effort to pave the way for useful and fruitful EU-funded BCI research for the next ten years

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