Graph theoretical analysis has recently been used to study brain function. This study aims to compare the functional brain networks derived from electroencephalography (EEG) of 10 patients suffering from epilepsy with 10 healthy subjects based on graph theory. Five epochs per healthy subject, and ten epochs (during epileptiform discharge and non-discharge) per patient were selected and analyzed using wavelet-crosscorrelation analysis. The clustering coefficient, characteristic path length, small-worldness, and nodal betweenness centrality were calculated using graph analysis. The results showed that in the patients, Wavelet-crosscorrelation Coefficients (WCC) were significantly higher, and clustering and path length were significantly lower during discharge compared with the healthy subjects, along with alterations in the hub regions. These results suggest a loss of small-world topology in the functional brain network of epilepsy patients. A loss of small-world topology was found even during non-discharge, therefore network indices might aid to distinguish epilepsy patients from healthy individuals.
The establishment of methods for recording and analyzing the dynamic changes in the human affective responses is important for understanding and utilizing affective dynamics. This study analyzes the structure of sensory and affective responses recorded through the temporal dominance (TD) method, which is an effective dynamic sensory appraisal approach. Temporal evolution is modeled by state-space local-level equations, and the structure of the responses is estimated based on the covariance matrix of the disturbances of the state variables. This approach is applied to the TD responses for strawberries recorded in a previous study, and its validity is examined.