NDLI: The BCI competition III: validating alternative approaches to actual BCI problems

Content Provider	IEEE Xplore Digital Library
Author	Blankertz, B. Muller, K.-R. Krusienski, D.J. Schalk, G. Wolpaw, J.R. Schlogl, A. Pfurtscheller, G. Millan, Jd.R. Schroder, M. Birbaumer, N.
Copyright Year	2001
Abstract	A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. 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.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	7
File Size	319143
Starting Page	153
Ending Page	159
File Format	PDF
ISSN	15344320
Volume Number	14
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Control systems Brain computer interfaces Programmable control Adaptive control Laboratories Signal analysis Machine learning Pattern classification Classification algorithms Machine learning algorithms slow cortical potentials Augmentative communication beta rhythm brain–computer interface (BCI) electroencephalography (EEG) ERP imagined hand movements mu rhythm nonstationarity P300 rehabilitation single-trial classification
Content Type	Text
Resource Type	Article
Subject	Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications

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