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

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 11; p. 3028
• Main Authors: Li, Zina, Qiu, Lina, Li, Ruixin, He, Zhipeng, Xiao, Jun, Liang, Yan, Wang, Fei, Pan, Jiahui
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.05.2020; MDPI
• Subjects: Accuracy; Affect (Psychology); Algorithms; brain-computer interface (BCI); Brain-Computer Interfaces; Classification; Datasets; Electroencephalography; electroencephalography (EEG); emotion recognition; Emotions; Emotions - classification; Experiments; feature selection; Humans; Optimization; particle swarm optimization (PSO); Physiology; Principal components analysis; Self evaluation; Support Vector Machine; Support vector machines; electroencephalography (EEG); particle swarm optimization (PSO); emotion recognition; brain-computer interface (BCI); feature selection

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 s...