EEG-based emotion classification using a deep neural network and sparse autoencoder

Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su ORCID: https://orcid.org/0000-0002-6618-7483, Li, Wei and Bi, Yifei (2020) EEG-based emotion classification using a deep neural network and sparse autoencoder. Frontiers in Systems Neuroscience, 14.

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Abstract

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.

Item Type: Article
Additional Information: © 2020 Liu, Wu, Luo, Qiu, Yang, Li and Bi. This is the final version of record of the following article: [Liu J, Wu G, Luo Y, Qiu S, Yang S, Li W and Bi Y (2020) EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder. Front. Syst. Neurosci. 14:43. doi: 10.3389/fnsys.2020.00043] which was originally published by Frontiers Media on 2 September 2020 [doi: 10.3389/fnsys.2020.00043].
Uncontrolled Keywords: EEG, emotion recognition, convolutional neural network, sparse autoencoder, deep neural network
Subjects: Computing
Related URLs:
Depositing User: Su Yang
Date Deposited: 02 Jun 2021 15:20
Last Modified: 02 Jun 2021 15:21
URI: http://repository.uwl.ac.uk/id/eprint/7922

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