Single sweep analysis of visual evoked potentials through a model of parametric identification

Abstract

An original method is presented for the single sweep analysis of visual evoked potentials (VEP's). The introduced algorithm bases upon an AutoRegressive with eXogenous input (ARX) modeling. A Least Squares procedure estimates the coefficients of the model and allows to obtain a complete black-box description of the signal generation mechanism, besides providing a filtered version of the single sweep potential. The performance of the algorithm is verified on proper simulation tests and the experimental results put into evidence the noticeable improvement of signal-to-noise ratio with a consequent better recognition of the classical parameters of the peaks (latencies and amplitudes). The possibility of measuring these parameters on a single sweep basis enables to evaluate the dynamics of the Central Nervous System response during the entire course of the examination. A classification of the estimated evoked potentials in a small number of subsets, on the basis of their morphology, is also possible.

Some considerations on estimating event-related brain signals

“…Cerutti et al (1987 a) and Chiarenza et al (1987) modelled the baseline-EEG using an AR-process attempted to estimate and subtract the background-EEG after presentation of the stimulus. They assumed stationarity of the background-EEG for the whole recording epoch.…”

Spatial and Temporal Distribution Enhancement of Movement-Related Brain Macropotentials

“…According to (27), in the identification procedure the parameter k 1 has been chosen equal to Ϫn b /2. The validity of the identification procedure is tested by checking the whiteness of the equation error (k) by means of the Anderson's test with the 95% confidence interval (18).…”

Automatic Artefact Removal from Event-related Potentials via Clustering

“…Methods for denoising include Wiener filtering [4] and autoregressive modelling [5]. More recently wavelets started becoming increasingly more popular for this application [2,3,6], as they can cope with the nonstationary and non-linear nature of the ERPs much better than other methods.…”