EVNCERS: An Integrated Eigenvector Centrality-Variational Nonlinear Chirp Mode Decomposition-Based EEG Rhythm Separation for Automatic Emotion Recognition

Affective computing, which focuses on identifying emotions from physiological data, namely electroencephalography (EEG) is becoming increasingly significant. However, direct analysis of EEG is highly challenging due to its nonlinear and nonstationary character. Various EEG rhythms provide a reliable method for the automatic recognition of emotions. Therefore, an integrated eigenvector centrality-variational nonlinear chirp mode decomposition-based EEG rhythm separation (EVNCERS) is developed. For selecting the dominant channels, the eigenvector centrality method (EVCM) is used followed by variational nonlinear chirp mode decomposition (VNCMD) to retrieve the instantaneous frequency (IF) and instantaneous amplitude (IA) from EEG signals. The equivalent IA and IF are used to create the delta, theta, alpha, beta, and gamma rhythms. The rhythms are analyzed over several entropy-based features, chosen using statistical analysis [mean and standard deviation (STD)], then categorized using various machine-learning methods. The proposed EVNCERS has achieved the highest performance of accuracy and F1-score for arousal: (96.86%, 97.98%), valence: (96.87%, 97.82%), and dominance: (96.81%, 97.71%) using random rotation forest classifier. The performance revealed that the delta rhythm offered more insight into automatic emotion recognition. The DREAMER dataset results demonstrate that our model has the highest predictive ability, with area-under-the-curve (AUC) values of 0.98 for arousal and dominance and 0.99 for the valence category. The SEED dataset also shows a similar trend, with the delta rhythm achieving the highest accuracy of 87.25% and F1-score of 74.54%. The proposed EVNCERS model can help in real-time situations to automatically recognize affective emotions, which would give us a greater range of emotional states.