CMAC-based Computational Model of Affects (CCMA) for profiling emotion from EEG signals

Several studies have been performed to profile emotions using EEG signals through affective computing approach. It includes data acquisition, signal pre-processing, feature extraction and classification. Different combinations of feature extraction and classification techniques have been proposed. However, the results are subjective. Very few studies include subject-independent classification. In this paper, a new profiling model, known as CMAC-based Computational Model of Affects (CCMA), is proposed.), CMAC is presumed to be a reasonable model for processing EEG signals with its innate capabilities to solve non-linear problems through self-organization feature mapping (SOFM). Features that are extracted using CCMA are trained using Evolving Fuzzy Neural Network (EFuNN) as the classifier. For comparison, classification of emotions using features that are derived from power spectral density (PSD) was also performed. The results shows that the performance of using CCMA for profiling emotions outperforms the performance of classifying emotions from PSD features.