The present paper introduces a new approach to artifact identification from MEG recordings, based on the statistical technique of Independent Component Analysis. Using this method, we were able to isolate both eye movement and eye blinking artifacts, as well as cardiac, myographic, and respiratory artifacts.
The basic assumption made upon the data used in the study is that of independence between brain and artifact waveforms. In most cases this independence can be verified by the known differences in physiological origins of those signals. Nevertheless, in some event-related potential (ERP) studies (e.g. when using infrequent or painful stimuli), both the cerebral and ocular signals can be similarly time-locked to the stimulus. This local time dependence could in principle affect these particular ICA studies. However, as the independence between two signals is a measure of the similarity between their joint amplitude distribution and the product of each signal's distribution (calculated throughout the entire signal, and not only close to the stimulus applied), it can be expected that the very local relation between those two signals, during stimulation, will not affect their global statistical relation.