An enhanced fitness function to recognize unbalanced human emotions data

In cognitive science and human-computer interaction, automatic human emotion recognition using physiological stimuli is a key technology. This research considers two-class (positive and negative) of emotions recognition using electroencephalogram (EEG) signals in response to an emotional clip from the genres happy, amusement, sad, and horror. This paper introduces an enhanced fitness function named as eD-score to recognize emotions using EEG signals. The primary goal of this research is to assess how genres affect human emotions. We also analyzed human behaviour based on age and gender responsiveness. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), D-score Genetic Programming (DGP), and enhanced D-score Genetic Programming (eDGP) for classification of emotions. The analysis shows that for two class of emotion eDGP provides classification accuracy as 83.33%, 84.69%, 85.88%, and 87.61% for 50-50, 60-40, 70-30, and 10-fold cross-validations. Generalizability and reliability of this approach is evaluated by applying the proposed approach to publicly available EEG datasets DEAP and SEED. When participants in this research are exposed to amusement genre, their reaction is positive emotion. In compliance with the self-reported feelings, brain signals of 26-35 years of age group provided the highest emotional identification. Among genders, females are more emotionally active as compared to males. These results affirmed the potential use of our method for recognizing emotions.

Automated summary

This study focuses on automatic human emotion recognition using EEG signals and analyzes the impact of different genres, age, and gender on human emotions. The results show that the enhanced D-score Genetic Programming performs the best in classifying emotions, and the method's generalizability and reliability are validated through publicly available EEG datasets. The research also reveals that participants in the amusement genre exhibit positive emotions, with brain signals of the 26-35 age group showing the highest emotional identification, and females are more emotionally active compared to males. These findings confirm the potential utility of the method for emotion recognition.