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.
The results showed that the highest performance was obtained for eight electrode pairs, which were placed on two hemispheres symmetrically (Fp2 − Fp1, F3 − F4, C3 − C4, P3 − P4, O1 − O2, F7 − F8, T3 − T4, T5 − T6), when CWC was applied to gamma of non-averaged emotional EEG oscillations of 6 s. Our findings were listed as follows: (1) The best phase synchronization measurement was CWC; (2) Gamma was found to be highly sensitive to emotional states (P, N, and UP); (3) The highest brain activation was induced by pleasant pictures rather than other pictures in either N or UP; (4) The highest phase coherence values produced by gamma were observed at anterio-frontal (F3 − F4) and central (C3 − C4) regions; (5) Two contrasting emotional states (P and UP) were distinguished from each other clearly, whereas the emotional state of UP was not clearly distinguished from the state of N based on both phase synchronization level and inter-trial variability of the phase difference in gamma; (6) The level of hemispheric phase synchronization was changed by the state of emotion over the whole scalp; and (7) The most useful data mining method was MultilayerPerceptron which is a NN classifier.
In conclusion, we can propose the use of CWC to extract emotional features from high-frequency sub-band of non-averaged EEG oscillations. Considering two non-averaged EEG oscillations, mediated by affective pictures and recorded from right and left hemispheric regions, MPC and PLI can measure inter-trial variability of the phase difference, while CWC can measure phase synchronization level. Results show that high performance for classification of emotional states can be obtained when the level of phase synchronization is used to estimate strong features instead of the possible inter-trial variability of phase difference. Comparing CWC to CCF, we should consider the basic difference between FT and WT: It is known that high resolution can not be obtained by using FT in both time and frequency, whereas we can perfectly determine the useful resolution by using WT in time-frequency space. Therefore, FT-based CCF can not quantify the level of phase synchronization in comparison to WT-based CWC to investigate functional brain connectivity induced by static and visual emotional stimuli of 6 s. EEG oscillations can be considered as summation of inhibitory and excitatory postsynaptic nerve action potentials, because intrinsic cell currents are mostly activated by ionic membrane channels as a result of ongoing interactions between the thalamus and cortex. Both firing rate of individual neurons and dynamic interplay among neurons within the local neuronal assembly have the crucial role in processing neural information [54,55,56]. Considering right and left hemispheres, neural populations can oscillate simultaneously depending on depolarization phase initiated by nerve cell . The brain is capable of selectively integrate several neural populations during cognitive tasks in gamma [58, 62]. Gamma is also found to be coherent with spatiotemporal magnetic field [59, 63, 64]. In recent years, gamma has been frequently investigated to understand the mechanism of highly ordered cognitive functions such as emotional processes due to its high sensitivity to emotional stimuli by means of coherence [34, 60, 61, 66]. Our new results are compatible with these studies. Gamma has also been found to be sensitive to phase synchronization in detecting emotional responses of patients with alexithymia . In future work, we are going to compare controls to patients with schizophrenia with respect to the same experimental paradigm for emotion recognition based on both single-channel EEG complexity estimations and two-channel inter-hemispheric EEG synchronization estimations in phase.
In conclusion, inter-hemispheric neuronal functions, generated by long-duration affective stimuli, can be analyzed by examining CWC in gamma. The results support that there is a strong relationship between gamma-specific inter-neuronal functions and emotional states at almost each brain region, while anterio-frontal and central regions show relatively higher hemispheric asymmetry levels in gamma.