A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

Sensors

Hao-Heng Chen¹  Yong Liang¹  Rui Miao¹  Na Han²  Dong-Wei Chen³  Chun-Jian Deng³  Wei-Qi Yang³  Lan Huang³ 

[1] Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau 999078, China;School of Business, Beijing Institute of Technology, JinFeng Road, TangJiaWan Town, Zhuhai 519000, China;School of Electronic Information Engineering, University of Electronic Science and Technology of China, XueYuan Road, Shi Qi District, Zhongshan 528400, China;

关键词: emotion recognition;    feature extraction;    differential entropy;    linear discriminant analysis;    electroencephalography;   

DOI  :  10.3390/s19071631

来源: DOAJ

【 摘 要 】

Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.

【 授权许可】

Unknown