TY - JOUR T1 - The Wadsworth BCI Research and Development Program: At Home with BCI. JF - IEEE Trans Neural Syst Rehabil Eng Y1 - 2006 A1 - Theresa M Vaughan A1 - Dennis J. McFarland A1 - Gerwin Schalk A1 - Sarnacki, William A A1 - Krusienski, Dean J A1 - Sellers, Eric W A1 - Jonathan Wolpaw KW - Animals KW - Brain KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - New York KW - Research KW - Switzerland KW - Therapy, Computer-Assisted KW - Universities KW - User-Computer Interface AB -

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.

VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792301 IS - 2 ER - TY - JOUR T1 - The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. JF - IEEE Trans Biomed Eng Y1 - 2004 A1 - Benjamin Blankertz A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Theresa M Vaughan A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Schlögl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, T. A1 - Schröder, Michael A1 - Niels Birbaumer KW - Adult KW - Algorithms KW - Amyotrophic Lateral Sclerosis KW - Artificial Intelligence KW - Brain KW - Cognition KW - Databases, Factual KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Reproducibility of Results KW - Sensitivity and Specificity KW - User-Computer Interface AB - Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms. VL - 51 IS - 6 ER - TY - JOUR T1 - Brain-computer interface technology: a review of the first international meeting. JF - IEEE Trans Rehabil Eng Y1 - 2000 A1 - Jonathan Wolpaw A1 - Niels Birbaumer A1 - Heetderks, W J A1 - Dennis J. McFarland A1 - Peckham, P H A1 - Gerwin Schalk A1 - Emanuel Donchin A1 - Quatrano, L A A1 - Robinson, C J A1 - Theresa M Vaughan KW - Algorithms KW - Cerebral Cortex KW - Communication Aids for Disabled KW - Disabled Persons KW - Electroencephalography KW - Evoked Potentials KW - Humans KW - Neuromuscular Diseases KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/10896178 IS - 2 ER -