A subject-independent emotion recognition technique using Variational Mode Decomposition and Deep Neural Networks outperformed state-of-the-art techniques on the benchmark DEAP dataset.
A novel subject-independent emotion recognition model using VMD and Deep Neural Networks outperforms existing state-of-the-art techniques on the DEAP dataset.
Emotion recognition from Electroencephalography (EEG) is proved to be a good choice as it cannot be mimicked like speech signals or facial expressions. EEG signals of emotions are not unique and it varies from person to person as each one has different emotional responses to the same stimuli. Thus EEG signals are subject dependent and proved to be effective for subject dependent emotion recognition. However, subject independent emotion recognition plays an important role in situations like emotion recognition from paralyzed or burnt face, where EEG of emotions of the subjects before the incidents are not available to build the emotion recognition model. Hence there is a need to identify common EEG patterns corresponds to each emotion independent of the subjects. In this paper, a subject independent emotion recognition technique is proposed from EEG signals using Variational Mode Decomposition (VMD) as a feature extraction technique and Deep Neural Network as the classifier. The performance evaluation of the proposed method with the benchmark DEAP dataset shows that the combination of VMD and Deep Neural Network performs better compared to the state of the art techniques in subject-independent emotion recognition from EEG.
Pandey et al. (2019) studied Emotion recognition. Variational Mode Decomposition (VMD) and Deep Neural Network vs. State of the art techniques was evaluated on Subject-independent emotion recognition performance. A subject-independent emotion recognition technique using Variational Mode Decomposition and Deep Neural Networks outperformed state-of-the-art techniques on the benchmark DEAP dataset.