Abstract:
This paper investigates the efficacy of the geneticbased
learning classifier system XCS, for the classification of
noisy, artefact-inclusive human electroencephalogram (EEG)
signals represented using large condition strings (108bits). EEG
signals from three participants were recorded while they
performed four mental tasks designed to elicit hemispheric
responses. Autoregressive (AR) models and Fast Fourier
Transform (FFT) methods were used to form feature vectors
with which mental tasks can be discriminated. XCS achieved a
maximum classification accuracy of 99.3% and a best average of
88.9%. The relative classification performance of XCS was then
compared against four non-evolutionary classifier systems
originating from different learning techniques. The experimental
results will be used as part of our larger research effort
investigating the feasibility of using EEG signals as an interface
to allow paralysed persons to control a powered wheelchair or
other devices.
