2008 Volume 20 Issue 1 Pages 117-128
In fractal analyses of EEG (Electroencepharography) and MEG (Magnetoencepharography) signals, the correlation dimension based on the correlation integral has usually been applied so far. In this research, we use the graph dimension obtained by the time-series fractal analysis by Higuchi's algorithm, and evaluate the MEG recordings for mentally stable 8 subjects and unstable 8 ones chosen among 23 subjects based on the result of psychological test. The fractal dimensions to the total 64 channels were determined every subject based on the relation between the coarsened time scale and the corresponding coarsened time-series length for the MEG data. In this result we found a multiple overlap structure of fractal dimensions (from the minimum dimension Dmin to the maximum one Dmax), so that we introduced its measure, ΔD= Dmax-Dmin. MEG recording during 3 sessions (10 minutes each) was executed for each subject. In session 1 a subject had remembered his/her daily events for 10 minutes. Continuously in sessions 2 and 3 the subject had remembered the contents of video after watching a cheerful video footage and a horrible and frightening video footage (2 minutes each), respectively. Mutual subtraction among three ΔD values by these 3 sessions enables to eliminate the noise under measurements and the personal equations. Owing to this method we could catch the variance of fractal structure in MEG data induced by different emotional stimuli, and revealed the difference between the mentally stable and unstable groups through the statistical examination. Not only the above discrete (scalar) application for each channel, the application of channel vector which consists of plural channels was examined. As the selection way of plural channels, two cases were considered, one is intra-area selection and the other is inter-area selection from the areas in cerebral cortex. As a result we could improve the discrimination ability of our method for the mentally stable and unstable groups by integrating the features of selected channels. Furthermore, we divided the MEG data in four time stages and evaluated the time dependency of fractal structure. With this analysis, there seemed that video's influence on the stable group subjects keeps fading away in time, but that on the unstable group subjects is long remaining.
