A maximum likelihood estimator for habituation effects in evoked magnetic field data

The standard procedure to determine the brain response from a multi-trial evoked MEG or EEG data set is to average the individual trials of these data, time locked to the stimulus onset times. When the brain responses vary from trial to trial this approach is false. In this paper a maximum likelihood estimator is derived for the case that the recorded data contain amplitude habituation. The estimator accounts for spatially and temporally correlated background noise that is superimposed on the brain response. The model is applied to a series of MEG data of median nerve stimulation. These data show that the late components (30-120 ms) show a systematic decrease in stimulus amplitude.