Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection

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.

Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification

“…Seminal contributions were made in 11 , which discussed the multi-class classification technique as a way to improve accuracy. At the outset, the researchers extracted multiple features from the time-domain of EEG signals, the frequency-domain, and time-frequency features.…”

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

“…With the proposed classifiers (ODCNN, SVM, and ERT), the achieved accuracy is 99.8%, 83.5%, and 71.4%, respectively, for the emotional states of neutral, happy, excited, and angry. According to Li et al [ 5 ], a brain–computer interface-based emotion recognition scheme with an improved particle swarm optimization for feature selection was employed with an accuracy of 95%. Graterol et al [ 60 ] proposed a method for emotion recognition and achieved an accuracy of around 53% for classification tasks.…”

“…Organizational competitiveness and employee relations are no exception. In recent years, researchers have frequently used emotional stimuli, such as images, sounds, and videos, to elicit subjects’ emotions and analyze their physiological signals to determine the regularity of emotional changes [ 4 , 5 ]. Human emotion recognition is accomplished through either a face recognition system or sensor-based systems.…”

Advances in Multimodal Emotion Recognition Based on Brain–Computer Interfaces

“…Selecting features with a strong ability to represent emotional state is highly important in emotion recognition tasks. Many algorithms exist that can reduce dimensionality by removing redundant or irrelevant features, such as particle swarm optimization (PSO) [ 122 ] and the genetic algorithm (GA) [ 123 ].…”