EEG functional connectivity maps, showing the interactions between brain areas in context to the placement of electrodes, were used for the investigation and comparison of three different types of epileptiform activity defined as single spike, spike followed by slow wave and repetitive spike. A nonlinear data-driven method was used to extract connectivity matrices that helped to identify network synchronization based on the number of connections for all brain regions, as represented by the 10-20 EEG system. This quantification was used to assess these three types of spike patterns in relation to the type of seizure, focal or generalized. Results showed some differences between connectivity patterns of single spikes related to focal epilepsy and connectivity patterns of repetitive spikes related to generalized epilepsy. The variance statistical analysis reported a significant difference (P - value ≪ 0.001) between single spike connectivity maps and other spike types. The results obtained, augment the prospects for diagnosis and enhance recognition of disease type via EEG-based connectivity maps.