Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network

Shuaiqi Liu, Xu Wang, Ling Zhao, Jie Zhao, Qi Xin*, Shui Hua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)

Abstract

Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.

Original languageEnglish
Pages (from-to)1710-1721
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume18
Issue number5
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Convolutional neural network
  • dynamic differential entropy
  • empirical mode decomposition
  • subject-independent emotion recognition

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