A Cognitive-Affective Measurement Model Based on the 12-Point Affective Circumplex

In the existing studies, the quantification of human affect from brain signals is not precise because it is merely rely on some approximations of the models from different affective modalities rather than the neurophysiology of emotions. Therefore, the objective of this study is to investigate the cognitive-affective model for quantifying emotions based on the brain activities through electroencephalogram (EEG). For that purpose, the recalibrated Speech Affective Space Model (rSASM) and the 12-Pont Affective Circumplex (12-PAC) were compared. Moreover, Kernel Density Estimation (KDE) and Mel-Frequency Cepstral Coefficients (MFCC) were used for feature extractions and Multi-Layer Perceptron (MLP) neural network was employed as the classifier. The results show that the MFCC-12PAC cognitive-affective model is the best model for all subjects. Furthermore, the results indicate that emotions are unique between participants and consistent throughout performing executive function tasks. Therefore, our empirical work has provided evidences that 12-PAC model may be adapted to improve the quantification of human affects from the brain signals. The analysis may be later expanded for the construction of an automated tool for the understanding of children's emotion during intervention sessions with psychologists.