Systematic Review: Emotion Recognition Based on Electrophysiological Patterns for Emotion Regulation Detection

Abstract

The electrophysiological basis of emotion regulation (ER) has gained increased attention since efficient emotion recognition and ER allow humans to develop high emotional intelligence. However, no methodological standardization has been established yet. Therefore, this paper aims to provide a critical systematic review to identify experimental methodologies that evoke emotions and record, analyze and link electrophysiological signals with emotional experience by statistics and artificial intelligence, and lastly, define a clear application of assessing emotion processing. A total of 42 articles were selected after a search based on six scientific browsers: Web of Science, EBSCO, PubMed, Scopus, ProQuest and ScienceDirect during the first semester of 2020. Studies were included if (1) electrophysiological signals recorded on human subjects were correlated with emotional recognition and/or regulation; (2) statistical models, machine or deep learning methods based on electrophysiological signals were used to analyze data. Studies were excluded if they met one or more of the following criteria: (1) emotions were not described in terms of continuous dimensions (valence and arousal) or by discrete variables, (2) a control group or neutral state was not implemented, and (3) results were not obtained from a previous experimental paradigm that aimed to elicit emotions. There was no distinction in the selection whether the participants presented a pathological or non-pathological condition, but the condition of subjects must have been efficiently detailed for the study to be included. The risk of bias was limited by extracting and organizing information on spreadsheets and participating in discussions between the authors. However, the data size selection, such as the sample size, was not considered, leading to bias in the validity of the analysis. This systematic review is presented as a consulting source to accelerate the development of neuroengineering-based systems to regulate the trajectory of emotional experiences early on.