Abstract

 Human Emotion Recognition is of vital importance to realize human-computer interaction (HCI), while multichannel electroencephalogram (EEG) signals gradually replace other physiological signals and become the main basis of emotional recognition research with the development of brain-computer interface (BCI). However, the accuracy of emotional classification based on EEG signals under video stimulation is not stable, which may be related to the characteristics of EEG signals before receiving stimulation. In this study, we extract the change of Differential Entropy (DE) before and after stimulation based on wavelet packet transform (WPT) to identify individual emotional state. Using the EEG emotion database DEAP, we divide the experimental EEG data in the database equally into 15 sets and extract their differential entropy on the basis of WPT. Then we calculate value of DE change of each separated EEG signal set. Finally, we divide the emotion into four categories in the two-dimensional valence-arousal emotional space by combining it with the integrated algorithm, Random Forest (RF). The simulation results show that the WPT-RF model established by this method greatly improves the recognition rate of EEG signal, with an average classification accuracy of 87.3%. In addition, we use WPT-RF model to train individual subjects, and the classification accuracy reached 97.7%.
Highlights
In recent years, intelligent computing technology has brought forth new ideas in the field of biomedicine, and many scientific research problems have been solved in the cross-collision of the two fields such as the image analysis and EEG calculation in biomedicine
Based on the above research status, this paper proposes an emotional classification model based on wavelet packet transform and Random Forest classifier, which divides human emotions into four categories in two-dimensional space
In order to verify the effectiveness of the methods repeated n times
Summary
Intelligent computing technology has brought forth new ideas in the field of biomedicine, and many scientific research problems have been solved in the cross-collision of the two fields such as the image analysis and EEG calculation in biomedicine. Image analysis is an effectiveness method for assistant diagnosis. In document [36], researchers used intelligent computer technology to recognize fluorescent signals and successfully solved the problem of poor human perception of fluorescent signals under the influence of environment. Richhariya et al [29] proposed an assistant recognition method based on computational intelligen, and realized automatic classification of X-ray images for disease diagnosis. EEG is wildly used in the biomedicine field. It is an important method for HCI systems and diagnosis of some diseases such as Depression, Hyperactivity, Schizophrenia and so on. Acharya et al [1] developed a computer-aided detection (cad) system to assist EEG signal detection, and successfully applied it to epilepsy localization
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