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.

Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.