Four asymmetry measurements (conventional coherence function (CCF), cross wavelet correlation (CWC), phase lag index (PLI), and mean phase coherence (MPC)) have been compared to each other for the first time in order to recognize emotional states (pleasant (P), neutral (N), unpleasant (UP)) from controls in EEG sub-bands (delta (0-4 Hz), theta (4-8 Hz), alpha (8-16 Hz), beta (16-32 Hz), gamma (32-64 Hz)) mediated by affective pictures from the International Affective Picture Archiving System (IAPS). Eight emotional features, computed as hemispheric asymmetry between eight electrode pairs (Fp1 - Fp2, F7 - F8, F3 - F4, C3 - C4, T7 - T8, P7 - P8, P3 - P4, and O1 - O2), have been classified by using data mining methods. Results show that inter-hemispheric emotional functions are mostly mediated by gamma. The best classification is provided by a neural network classifier, while the best features are provided by CWC in time-scale domain due to non-stationary nature of electroencephalographic (EEG) series. The highest asymmetry levels are provided by pleasant pictures at mostly anterio-frontal (F3 - F4) and central (C3 - C4) electrode pairs in gamma. Inter-hemispheric asymmetry levels are changed by each emotional state at all lobes. In conclusion, we can state the followings: (1) Nonlinear and wavelet transform-based methods are more suitable for characterization of EEG; (2) The highest difference in hemispheric asymmetry was observed among emotional states in gamma; (3) Cortical emotional functions are not region-specific, since all lobes are effected by emotional stimuli at different levels; and (4) Pleasant stimuli can strongly mediate the brain in comparison to unpleasant and neutral stimuli.