Independent Component Analysis for identification of artifacts in Magnetoencephalographic recordings Ricardo Vigario 1 ; Veikko J ousmiiki2 , Matti Hiimiiliiinen2, Riitta Hari 2 , and Erkki Oja 1 1 Lab. of Computer & Info. Science Helsinki University of Technology P.O. Box 2200, FIN-02015 HUT, Finland {Ricardo.Vigario, Erkki.Oja}@hut.fi 2 Brain Research Unit, Low Temperature Lab. Helsinki University of Technology P.O. Box 2200, FIN-02015 HUT, Finland {veikko, msh, hari}@neuro.hut.fi Abstract We have studied the application of an independent component analysis (ICA) approach to the identification and possible removal of artifacts from a magnetoencephalographic (MEG) recording. This statistical tech- nique separates components according to the kurtosis of their amplitude distributions over time, thus distinguishing between strictly periodical signals, and regularly and irregularly occurring signals. Many artifacts belong to the last category. In order to assess the effectiveness of the method, controlled artifacts were produced, which included saccadic eye movements and blinks, increased muscular tension due to biting and the presence of a digital watch inside the magnetically shielded room. The results demonstrate the capability of the method to identify and clearly isolate the produced artifacts. 1 Introduction When using a magnetoencephalographic (MEG) record, as a research or clinical tool, the investigator may face a problem of extracting the essential features of the neuromagnetic • Corresponding author