PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The BCI competition III: Validating alternative approachs to actual BCI problems.
Benjamin Blankertz, Klaus-Robert Müller, Dean Krusienski, Gerwin Schalk, Jonathan Wolpaw, Alois Schlögl, Gert Pfurtscheller, José del R Millán, Michael Schroeder and Niels Birbaumer
Trans. Neural Sys. Rehab. Eng. Volume 14, Number 2, pp. 153-159, 2006.


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. This article describes the data sets that were provided to the competitors and gives an overview of the results.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:2258
Deposited By:Benjamin Blankertz
Deposited On:11 October 2006