Research of electroencephalography representational emotion recognition based on deep belief networks

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 35; no. 2; p. 182
• Main Authors: Yang, Hao, Zhang, Junran, Jiang, Xiaomei, Liu, Fei
• Format: Journal Article
• Language: Chinese
• Published: China Sichuan Society for Biomedical Engineering 25.04.2018
• Subjects: Accuracy; Algorithms; Belief networks; Brain; Classification; Data processing; EEG; Electroencephalography; Emotion recognition; Emotions; Entropy; Frequencies; Learning algorithms; Machine learning; Model accuracy; Signal processing; Support vector machines; deep belief network; deep learning; electroencephalogram; emotional recognition
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.201706035

In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accura