Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder

As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage th...

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Bibliographic Details
Published in:Frontiers in computational neuroscience 2021-10, Vol.15, p.743426-743426
Main Authors: An, Yanling, Hu, Shaohai, Duan, Xiaoying, Zhao, Ling, Xie, Caiyun, Zhao, Yingying
Format: Article
Language:English
Subjects:
Arousal
Camouflage
convolution neural network
differential entropy
EEG
Electroencephalography
emotion recognition
Emotions
Entropy
feature fusion
Learning algorithms
Machine learning
Neural networks
Neuroscience
stacked autoencoder
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Summary:As one of the key technologies of emotion computing, emotion recognition has received great attention. Electroencephalogram (EEG) signals are spontaneous and difficult to camouflage, so they are used for emotion recognition in academic and industrial circles. In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm based on 3D feature fusion and convolutional autoencoder (CAE). First, the differential entropy (DE) features of different frequency bands of EEG signals are fused to construct the 3D features of EEG signals, which retain the spatial information between channels. Then, the constructed 3D features are input into the CAE constructed in this paper for emotion recognition. In this paper, many experiments are carried out on the open DEAP dataset, and the recognition accuracy of valence and arousal dimensions are 89.49 and 90.76%, respectively. Therefore, the proposed method is suitable for emotion recognition tasks.
ISSN:1662-5188
1662-5188
DOI:10.3389/fncom.2021.743426