Causality Analysis of Alpha Activities by Multidimensional Directed Coherence

Abstract

Alpha activities as a basic component of EEG (electroencephalogram) are mainly observed with eye-closed and reported state, and indicates rhythmic and diffused pattern on the scalp. Therefore analysis of the relation among many sequences of alpha activities measured at different positions on the scalp is expected to be useful not only for diagnosing psychiatric disease, but also for investigating mechanism of brain information processing by means of causality analysis, that is, macroscopic estimation of flow pattern within brain. Although directed coherence analysis has been proposed as a method for estimating the direction and magnitude of information flow between two sequences, superposition of results for each pair of sequences can not represent true relation among the whole sequences.
In this paper, we proposed the multidimensional directed coherence analysis by modifying two-channel formula in order to apply it to the analysis of multi-channel sequence of alpha activities. Results of simulation revealed that multidimensional directed coherence can indicate more quantitatively the relation among the multi-channel sequences compare with conventional two-channel formula Moreover, the proposed method was applied to the analysis of EEG data of normal volunteer and patient. Results show that the method can provide a useful diagnostic information by assessment of the signal flow patter within brain.