Abstract
Electroencephalogram (EEG) signals have been widely used in emotion recognition. However, the current EEG-based emotion recognition has low accuracy of emotion classification, and its real-time application is limited. In order to address these issues, in this paper, we proposed an improved feature selection algorithm to recognize subjects’ emotion states based on EEG signal, and combined this feature selection method to design an online emotion recognition brain-computer interface (BCI) system. Specifically, first, different dimensional features from the time-domain, frequency domain, and time-frequency domain were extracted. Then, a modified particle swarm optimization (PSO) method with multi-stage linearly-decreasing inertia weight (MLDW) was purposed for feature selection. The MLDW algorithm can be used to easily refine the process of decreasing the inertia weight. Finally, the emotion types were classified by the support vector machine classifier. We extracted different features from the EEG data in the DEAP data set collected by 32 subjects to perform two offline experiments. Our results showed that the average accuracy of four-class emotion recognition reached 76.67%. Compared with the latest benchmark, our proposed MLDW-PSO feature selection improves the accuracy of EEG-based emotion recognition. To further validate the efficiency of the MLDW-PSO feature selection method, we developed an online two-class emotion recognition system evoked by Chinese videos, which achieved good performance for 10 healthy subjects with an average accuracy of 89.5%. The effectiveness of our method was thus demonstrated.
Highlights
As an advanced function of the human brain, emotion plays an important role in daily life.Emotion recognition has high application value in the fields of commerce, medicine, education, and human-computer interaction, and has become a research area of great interest [1]
Our results show that the accuracy of EEG-based emotion recognition can be significantly improved by using our modified multi-stage linearly-decreasing inertia weight (MLDW)-particle swarm optimization (PSO) feature selection, and this modified MLDW-PSO can be used for online emotion recognition
The results show that the accuracy obtained using the Relief, standard PSO and MLDW-PSO feature selection methods are significantly higher than the individual features and the combination of all features without feature selection
Summary
As an advanced function of the human brain, emotion plays an important role in daily life. Emotion recognition has high application value in the fields of commerce, medicine, education, and human-computer interaction, and has become a research area of great interest [1]. Researchers usually use emotional materials, such as pictures, sounds, and videos, to induce subjects’. In the study of emotion recognition, two emotional dimensions of Russell’s Valence-Arousal emotion model are usually used for emotion evaluation [2]. Physiological signals, such as electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), galvanic skin response (GSR), and respiration rate (RR), are often used to reflect emotional states. The most commonly used is EEG, due to its good
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
