期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 18, 期 5, 页码 1710-1721出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.3018137
关键词
Electroencephalography; Emotion recognition; Feature extraction; Classification algorithms; Heuristic algorithms; Brain modeling; Frequency-domain analysis; Convolutional neural network; dynamic differential entropy; empirical mode decomposition; subject-independent emotion recognition
类别
资金
- Natural Science Foundation of China [61401308, 61572063]
- Natural Science Foundation of Hebei Province [F2020201025, F2019201151, QN2017306, F2018210148]
- Science research project of Hebei Province [QN2020030, QN2016085]
- Foundation of President of Hebei University [XZJJ201909]
- High-Performance Computing Center of Hebei University
Affective computing is a key technology for advanced brain-machine interfaces, and subject-independent emotion recognition remains a challenge. This study proposes a subject-independent emotion recognition algorithm based on dynamic differential entropy, which enhances accuracy through optimized feature extraction and classification methods, and discusses the optimal electrode placement for reducing complexity.
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.
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