CMAC-Based Computational Model of Affects (CCMA) from self-organizing feature mapping weights for classification of emotion using EEG signals - Yaacob, Hamwira Sakti; Abdul Rahman, Abdul Wahab; Kamaruddin, Norhaslinda (International Society for Computers and Their Applications, 2015)
Emotion is postulated to be generated at the brain. To capture the brain activities during emotional processing, several neuro-imaging techniques have been adopted, including electroencephalogram (EEG). In the existing studies, different techniques have been employed to extract features from EEG signals for emotion classification. However, existing feature extraction techniques do not consider spatial and temporal neural-dynamics of emotion. Furthermore, the non-linearity of EEG and self-adaptive of neural activations are disregard. Therefore, the classification accuracy of any feature extraction technique is inconsistent when applied with different classifiers. Hence, in this study, a new feature extraction technique that inculcates the qualities of EEG signal and the behavior of neural activations is proposed based on Cerebellar Model Articulation Controller (CMAC) model. The accuracy of classifying calm, fear, happiness and sadness emotional states using Evolving Fuzzy Neural Network (EFuNN) classifiers are reported based on subject-dependent and subject-independent validations. The classification performance of using features from power spectral density (PSD), kernel density estimation (KDE) and mel-frequency ceptral coefficients (MFCC) are also compared and reported. It is observed that the proposed technique is able to yield accuracy of above 50% to above 90% for subject-dependent classification. For subject-independent approach, the highest accuracy is barely 40%. The results suggest that this approach is comparable as a feature extraction technique for classifying emotions.