Evidence of emotion-antecedent appraisal checks in electroencephalography and facial electromyography.

ABSTRACT: In the present study, we applied Machine Learning (ML) methods to identify psychobiological markers of cognitive processes involved in the process of emotion elicitation as postulated by the Component Process Model (CPM). In particular, we focused on the automatic detection of five appraisal checks-novelty, intrinsic pleasantness, goal conduciveness, control, and power-in electroencephalography (EEG) and facial electromyography (EMG) signals. We also evaluated the effects on classification accuracy of averaging the raw physiological signals over different numbers of trials, and whether the use of minimal sets of EEG channels localized over specific scalp regions of interest are sufficient to discriminate between appraisal checks. We demonstrated the effectiveness of our approach on two dat

SUBMITTER: Coutinho E 

PROVIDER: S-EPMC5749688 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature