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.
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.