Abstract
A parametric method of identification of movement-related brain macropotentials on a single trial basis through an ARX (autoregressive with exogenous inputs) algorithm is presented. The basic estimation of the information contained in the single trial is taken from an average carried out on a sufficient number of trials, while the noise sources, EEG and EOG are characterized as exogenous inputs in the model. The simulations as well as the experimental results confirm the capability of the model to drastically improve the signal/noise ratio in each single trial and to satisfactorily identify the contributions of signal and noise in the overall recording. This way, using the same algorithm, a particularly efficient reduction of ocular artifacts is also achieved. The movement-related brain macropotentials recorded in three subjects show a high degree of variability from trial to trial and this effect seems to be related to programming processes and evaluation of errors.
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