- In this paper we investigate the classification of mental
tasks based on electroencephalographic (EEG) data for
Brain Computer Interfaces (BCI) in two scenarios: off
line and on-line. In the off-line scenario we evaluate the
performance of a number of classifiers using a benchmark
dataset, the same pre-processing and feature selection and
show that classifiers that haven't been used before are
good choices. We also apply a new feature selection
method that is suitable for the highly correlated EEG data
and show that it greatly reduces the number of features
without deteriorating the classification accuracy. In the
on-line scenario that we have designed, we study the
performance of our system to play a computer game for
which the signals are processed in real time and the
subject receives visual feedback of the resulting control
within the game environment. We discuss the
performance and highlight important issues.
(from crpit.com)
(local if available)
