Analysis of MEG Inverse Problem Based on Fourth Order Statistics
S. Niijima, S. Ueno · Journal of the Magnetics Society of Japan · 2003
In recent years, several inverse solutions of magnetoencephalography (MEG) have been proposed. One of them, the multiple signal classification (MUSIC) method, utilizes spatiotemporal information obtained from magnetic fields. The conventional MUSIC method is, however, sensitive to Gaussian noise, and a sufficiently large signal-to-noise ratio (SNR) is required to estimate the number of sources and to specify the precise locations of electrical neural activities. In this paper, a universal fourth order MUSIC (UFO-MUSIC) method, which is based on fourth order statistics, is proposed. This method is shown to be more robust against Gaussian noise than the conventional MUSIC method. It is an algebraic approach to independent component analysis (ICA) . Although ICA and the analysis of the MEG inverse problem have been separately discussed, the proposed method incorporates ICA into the MEG inverse solution. The results of numerical simulations demonstrate the validity of the proposed method.