Single-trial magnetoencephalographic data decomposition and localization based on independent component analysis approach

Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.