The human-computer connection : an overview of brain-computer interfaces

This article introduces the field of brain-computer interfaces (BCI), which allows the control of devices without the generation of any active motor output but directly from the decoding of the user?s brain signals. Here we review the current state of the art in the BCI field, discussing the main components of such an interface and illustrating ongoing research questions and prototypes for controlling a large variety of devices, from virtual keyboards for communication to robotics systems to replace lost motor functions and even clinical interventions for motor rehabilitation after a stroke. The article concludes with some insights into the future of BCI

Recent and upcoming BCI progress: overview, analysis, and recommendations

Brain–computer interfaces (BCIs) are finally moving out of the laboratory and beginning to gain acceptance in real-world situations. As BCIs gain attention with broader groups of users, including persons with different disabilities and healthy users, numerous practical questions gain importance. What are the most practical ways to detect and analyze brain activity in field settings? Which devices and applications are most useful for different people? How can we make BCIs more natural and sensitive, and how can BCI technologies improve usability? What are some general trends and issues, such as combining different BCIs or assessing and comparing performance? This book chapter provides an overview of the different sections of this book, providing a summary of how authors address these and other questions. We also present some predictions and recommendations that ensue from our experience from discussing these and other issues with our authors and other researchers and developers within the BCI community. We conclude that, although some directions are hard to predict, the field is definitely growing and changing rapidly, and will continue doing so in the next several years

On the Need for On-Line Learning in Brain-Computer Interfaces

In this paper we motivate the need for on-line learning in BCI and illustrate its benefits with the simplest method, namely fixed learning rates. However, the use of this method is supported by the risk of hampering the user to acquire suitable control of the BCI if the embedded classifier changes too rapidly. We report the results with 3 beginner subjects in a series of consecutive recording, where the classifiers are iteratively trained with the data of a given session and tested on the next session. At the end of these sessions 2 of the subjects reach a suitable performance that is close to allow them to start operating a brain-actuated device

Brain-Machine Interfaces: The Perception-Action Closed Loop

A brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people [1]-[5]. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop

Adaptive Brain Interfaces

Severely disabled people are largely excluded from the benefits information and communication technologies have brought to our industries, economies, appliances, and general quality of life. But what if that technology would allow them to communicate their wishes or control electronic devices directly through their thoughts alone? This is the goal and promise of the Adaptive Brain Interfaces (ABI) project, which aims to augment natural human capabilities by enabling people to interact with computers (after a brief training period) through the direct control of their thoughts

Adaptive Brain Interfaces for Communication and Control

This paper describes our work on a portable non-invasive brain-computer interface (BCI), called Adaptive Brain Interfaces (ABI), that analysis online the users spontaneous electroencephalogram (EEG) signals from which a neural classifier recognizes 3 different mental states. The outputs of the classifier are used as mental commands to operate communication and control devices. Although still at a research stage, BCIs offer the possibility to augment human capabilities in a natural way and are particularly relevant as an aid for paralyzed humans

BMI: Lessons from Tests with Impaired Users

Brain-machine interfaces (BMI) have largely been demonstrated in laboratory conditions involving, mainly, healthy users. We have recently carried out a series of studies with a substantial number of motor-disabled end-users operating different brain-controlled devices in ecological conditions and without the assistance of BMI experts