The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.

TitleThe BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.
Publication TypeJournal Article
Year of Publication2004
AuthorsBlankertz, B, Müller, K-R, Curio, G, Vaughan, TM, Schalk, G, Wolpaw, JR, Schlögl, A, Neuper, C, Pfurtscheller, G, Hinterberger, T, Schröder, M, Birbaumer, N
JournalIEEE transactions on bio-medical engineering
Volume51
Pagination1044–1051
Date Published06/2004
ISSN0018-9294
Keywordsaugmentative communication, BCI, beta-rhythm, brain-computer interface, EEG, ERP, imagined hand movements, lateralized readiness potential, mu-rhythm, P300, Rehabilitation, single-trial classification, slow cortical potentials
Abstract

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.

URLhttp://www.ncbi.nlm.nih.gov/pubmed/15188876
DOI10.1109/TBME.2004.826692