Abstract
In BCI (Brain Computer Interface) research, the
classification of EEG signals is a domain where raw
data has to undergo some preprocessing, so that the
right attributes for classification are obtained.
Several transformational techniques have been used for
this purpose: Principal Component Analysis, the
Adaptive Autoregressive Model, FFT or Wavelet
Transforms, etc. However, it would be useful to
automatically build significant attributes appropriate
for each particular problem. we use Genetic Programming
to evolve projections that translate EEG data into a
new vectorial space (coordinates of this space being
the new attributes), where projected data can be more
easily classified. Although our method is applied here
in a straightforward way to check for feasibility, it
has achieved reasonable classification results that are
comparable to those obtained by other state of the art
algorithms. In the future, we expect that by choosing
carefully primitive functions, Genetic Programming will
be able to give original results that cannot be matched
by other machine learning classification algorithms.
Users
Please
log in to take part in the discussion (add own reviews or comments).