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Appl. Sci. 2017, 7(10), 1060; doi:10.3390/app7101060

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

1
Institute of International WIC, Beijing University of Technology, Beijing 100124, China
2
Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China
3
Knowledge Information Systems Lab, Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan
*
Author to whom correspondence should be addressed.
Received: 11 September 2017 / Accepted: 11 October 2017 / Published: 13 October 2017
(This article belongs to the Special Issue Smart Healthcare)
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Abstract

The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%. View Full-Text
Keywords: emotion recognition; EEG signal; multidimensional features; hybrid neural networks; CNN; LSTM RNN emotion recognition; EEG signal; multidimensional features; hybrid neural networks; CNN; LSTM RNN
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Li, Y.; Huang, J.; Zhou, H.; Zhong, N. Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks. Appl. Sci. 2017, 7, 1060.

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