Application of Ica to Meg Noise Reduction

It is important to reduce noise in MEG measurement, since the signal to noise ratio is smaller than 1.0, even in magnetically shielded room environment. ICA is a powerful tool for noise reduction in MEG measurements. We have applied ICA to various MEG data. By using ICA, we can remove the cardiac field, power line noise and other noises from MEG data. Also, we succeeded in extracting auditory evoked field from non-averaged MEG data. ICA produces many independent components in MEG, but usually their classification into relevant and irrelevant components depends largely on subjective judgment. We propose a criterion for judging which of the obtained independent components comprise MEG components, and in particular the evoked response using the signal subspace obtained from the averaged response. This method often worked effectively to reconstruct single evoked responses based on the objective criterion. Although there still remain many problems. The application of ICA to MEG data, should further be studied because ‘noninvasive’ study of the brain activities intrinsically implies ‘blind’ separation of activities.