Recently, neural networks have been used as a tool for the classificat
ion of spatio-temporal EEG patterns arising from a hand movement exper
iment. Results indicated that, based on single-trial EEG data recorded
before movement, the side of hand movement can be predicted with fair
ly high precision, but variability of results raised the question of t
heir validity. In order to validate results obtained with real EEG dat
a, an equivalent simulated movement experiment was performed. Alpha ba
nd rhythms composed of two components were simulated as a superpositio
n of two second-order autoregressive (AR) processes. These simulated E
EG data were then filtered, and their power values calculated and used
as features in a classification task. Systematic analysis of the sens
itivity of the classification results on various simulation parameters
was performed. The analysis showed that the Cascade-correlation (CC)
network is able to perform satisfactorily in an extremely noisy enviro
nment.