Artifacts are a common problemin signal processing. They usually consist of strong signals that
have to be removed before the data analysis. In this chapter we show how by applying Independent
Component Analysis artifact signals can be extracted easily from biomedical signals. Focussing
on movement artifacts in functional MRI data we demonstrate that artifact signals may contain
themselves important information about the experiments from which the data results. This is the
case for the eye movement signal that can be extracted directly from the functionalMRI data. We
describe the way to extract this signal with state of the art ICA algorithms and show how it can
be used to quantify eye movement in a fMRI experiment instead using a dedicated eye tracker.
Finally we present the FMREyetrack SPM Plugin that allows the user to automatically extract the
eye movement information from fMRI data sets.
Keywords: artifacts, feature extraction, ICA, fMRI, large scale biomedical data sets
