## 4 Discussion
### RQ6: What Are the Applications and Impacts of Assessing the Emotion Processing? Analyzing the electrophysiological basis of emotional processing and regulation may be a promising tool that finds applications in a wide variety of areas. For instance, advances in emotion recognition and classification by machine learning and neural networks algorithms can nowadays find utility in video gaming by personalization of the game environment to the user [94], customer satisfaction, high-quality services [95], and aggression predictions in video surveillance tapes [96]. Although a universal way of expressing emotions has been previously emphasized [97], cross-culture [98] and gender [34] variations make artificial intelligence engineering and emotion-related electrophysiological studies a relevant source of information for sociology and anthropology investigations. As regards neurocognitive applications, the literature reviewed here highlighted direct correlations between non-pathological traits of personality (e.g., neuroticism [58], motor performances [35] and executive functions skills) [31] and emotions perception or regulation, which can be useful information in areas such as job positioning or medical assessment of pathological predispositions. Upon medical applications, emotion recognition by artificial intelligence algorithms allows the rapid acquisition of information that could orient toward diagnosis indications. As a matter of fact, various of the reviewed studies highlighted specific profiles of electrophysiological indicators of emotional processing, allowing the significant distinction between pathological and non-pathological populations [28; 38; 47]. Nevertheless, none of the studies mentioned in this systematic review searched for in-between pathologies distinctions (e.g., the distinction between schizophrenia and autism spectrum disorder that are often confused [99]). Therefore, diagnosis methods based on electrophysiological correlates of emotion processing still lack investigations. Finally, as emotion processing disabilities are part of the symptomatology of multiple pathologies (e.g., autism spectrum disorder, schizophrenia, social anxiety disorder, attention deficit/hyperactivity disorder, anorexia nervosa, depression), the assessment of their electrophysiological correlates while attending or regulating emotional stimuli may be an effective way to evaluate the pathological evolution or the effectiveness of medical interventions. For instance, [37] observed a normalization (i.e., differences were not significant between control and experimental groups anymore) of the connectivity in the beta frequency range within cingulate/prefrontal, meso/lateral temporal, posterior parietal, and insular regions during an ER task after eight weeks of inpatient trauma treatment, which consisted of individual and group-based cognitive stabilization, and art interventions in patients experiencing complex trauma disorders. Another promising application of emotional electrophysiological correlates is the implementation of real-time biofeedback during training [100]. Only two studies included in the present review focused on the effect of neurofeedback on emotion processing or regulation abilities [43; 60], highlighting promising results of such a method. Particularly, although [43] did not observe a significant change in the SCR to emotional processing and regulation, they emphasized a reduction of seizure frequency and improvement of anxiety and depression traits after a 3-month skin-conductance-based biofeedback intervention in drug-resistant patients suffering from temporal lobe epilepsy. On the other hand, [60] were interested in the subjective and electrophysiological basis of emotional perception before and after a 5-week EEG-based neurofeedback intervention. They observed the improvement of negative ER and the restoration of delta wave lateralization after neurofeedback training. In sum, neurofeedback training has the potential to be considered as a valid clinical intervention for emotional and cognitive management.