A parametric method of identification of single-trial event-related potentials in the brain

A parametric method of identification of event-related (or evoked) potentials on a single-trial basis through an ARX (autoregressive with exogenous input) algorithm is discussed. 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 of drastically improving the S/N (signal-to-noise) ratio in each single trial and satisfactorily identifying the contributions of signal and noise to the overall recording. A particularly efficient reduction of ocular artifacts is also achieved.