Multidimensional directed information (MDI) analysis is a signal processing method to quantify and visualize the causality of multichannel time series in the form of information flow. MDI analysis is defined as conditional mutual information and needs large calculations. Although MDI is used for electroencephalogram (EEG) analysis, large computation time is a problem. MDI can be calculated without direct probability calculations, assuming that the multichannel time series has Gaussian profiles. However, the amount of calculation increases exponentially with increase in the number of channels. Such large calculations have prevented practical use of MDI analysis in medical fields such as clinical EEG analysis in which many multidimensional time series need to be processed. In this paper, we propose a new calculation approach to drastically decrease the calculation time of MDI analysis. The proposed method makes it possible to decrease the calculation time exponentially for multichannel time series that can be approximated with multidimensional autoregressive models.Osamu Sakata (Member) received the B.S., M.S., and Ph.D.