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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder
Frontiers in Systems Neuroscience, Volume: 14
Swansea University Author: Scott Yang Yang
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© 2020 Liu, Wu, Luo, Qiu, Yang, Li and Bi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)Download (2.44MB)
Emotion classification based on brain–computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a nov...
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Emotion classification based on brain–computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN.
EEG, emotion recognition, convolutional neural network, sparse autoencoder, deep neural network
College of Science
National Natural Science Foundation of China under Grant 61976063, the funding of Overseas 100 Talents Program of Guangxi Higher Education, research funds of Diecai Project of Guangxi Normal Univesity, Guangxi Key Lab of Multi-source Information Mining and Security (19-A-03-02) and Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, the Young and Middle-aged Teachers’ Research Ability Improvement Project in Guangxi Universities under Grant 2020KY02030, and the Innovation Project of Guangxi Graduate Education under Grant YCSW2020102