The BCI competition III: Validating alternative approaches to actual BCI problems. - Benjamin Blankertz; Müller, Klaus-Robert; Krusienski, Dean J; Gerwin Schalk; Jonathan Wolpaw; Schlögl, Alois; Pfurtscheller, Gert; Millán, José del R; Schröder, Michael; Niels Birbaumer (IEEE Trans Neural Syst Rehabil Eng, 2006)
A brain-computer interface (BCI) is a system that allows its users to control external devices with brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.
Keywords: Algorithms; Brain; Communication Aids for Disabled; Databases, Factual; Electroencephalography; Evoked Potentials; Humans; Neuromuscular Diseases; Software Validation; Technology Assessment, Biomedical; User-Computer Interface

Brain-computer interface technology: a review of the first international meeting. - Jonathan Wolpaw; Niels Birbaumer; Heetderks, W J; Dennis J. McFarland; Peckham, P H; Gerwin Schalk; Emanuel Donchin; Quatrano, L A; Robinson, C J; Theresa M Vaughan (IEEE Trans Rehabil Eng, 2000)
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.
Keywords: Algorithms; Cerebral Cortex; Communication Aids for Disabled; Disabled Persons; Electroencephalography; Evoked Potentials; Humans; Neuromuscular Diseases; Signal Processing, Computer-Assisted; User-Computer Interface