Emotion Recognition Based on Dynamic Energy Features Using a Bi-LSTM Network

• Published in: Frontiers in computational neuroscience Vol. 15; p. 741086
• Main Authors: Zhu, Meili, Wang, Qingqing, Luo, Jianglin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 21.02.2022; Frontiers Media S.A
• Subjects: Bi-LSTM; Datasets; Deep learning; dynamic energy feature; EEG; Electroencephalography; emotion recognition; Emotions; Energy; energy sequence; Long short-term memory; Neural networks; Neuroscience; Noise reduction; Switzerland; Bi-LSTM; energy sequence; emotion recognition; EEG; dynamic energy feature
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2021.741086

Among electroencephalogram (EEG) signal emotion recognition methods based on deep learning, most methods have difficulty in using a high-quality model due to the low resolution and the small sample size of EEG images. To solve this problem, this study proposes a deep network model based on dynamic energy features. In this method, first, to reduce the noise superposition caused by feature analysis and extraction, the concept of an energy sequence is proposed. Second, to obtain the feature set reflecting the time persistence and multicomponent complexity of EEG signals, the construction method of the dynamic energy feature set is given. Finally, to make the network model suitable for small datasets, we used fully connected layers and bidirectional long short-term memory (Bi-LSTM) networks. To verify the effectiveness of the proposed method, we used leave one subject out (LOSO) and 10-fold cross validation (CV) strategies to carry out experiments on the SEED and DEAP datasets. The experimental results show that the accuracy of the proposed method can reach 89.42% (SEED) and 77.34% (DEAP).