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

Youjun Li, Jiajin Huang, Haiyan Zhou, Ning Zhong
2017 Applied Sciences  
Featured Application: The method presented in this study can be applied in many fields, such as mental health care, entertainment consumption behavior, society safety, and so on. For example, in the mental health care field, an automatic emotion analysis system can be constructed with our method to monitor the emotional variation of the subjects. With accurate and objective emotion analysis results from EEG signals, our method can provide useful treatment effect information to the medical
more » ... 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%.
doi:10.3390/app7101060 fatcat:beajya3p3ffttjsun4s2wg74oi