EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Junxiu Liu, Guopei Wu, Yuling Luo, Senhui Qiu*, Su Yang, Wei Li, Yifei Bi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

147 Citations (Scopus)


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.

Original languageEnglish
Article number43
JournalFrontiers in Systems Neuroscience
Publication statusPublished - 2 Sept 2020
Externally publishedYes


  • EEG
  • convolutional neural network
  • deep neural network
  • emotion recognition
  • sparse autoencoder


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