Development of fast analysis system for MEG data using independent component analysis

摘要

The Independent Component Analysis (ICA) for Magnetoencephalography (MEG) data using Message Passing Interface (MPI) has been developed for the purpose of the fast evaluation system for brain function. The MEG has the ability to localize cerebral current sources non-invasively, since the MEG has not only fine temporal resolution but also a high degree of spatial resolution, compared with other measurement devices. The MEG data is composed of complex brain signals and much noise. We have extracted stastically independent component from the mixed waves using ICA. Moreover, we have adopted the parallel computing technique with MPI in order to reduce the calculation time, which increases due to the tremendous amount of MEG data and calculation of ICA.