EEG-Based Emotion Classification Using Spiking Neural Networks

IEEE Access

Yi Cao¹  Juntao Xie²  Xuemei Ding³  Junxiu Liu⁴  Frank Jiang⁴  Yuling Luo⁴  Guopei Wu⁴  Qiang Fu⁴  Yunbai Qin⁴ 

[1] Business School, The University of Edinburgh, Edinburgh, U.K.;Department of Security, Guangxi Normal University, Guilin, China;School of Computing, Engineering, and Intelligent Systems, Ulster University, Londonderry, U.K.;School of Electronic Engineering, Guangxi Normal University, Guilin, China;

关键词: Emotion classification;    spiking neural network;    EEG signal;   

DOI  :  10.1109/ACCESS.2020.2978163

来源: DOAJ

【 摘 要 】

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications.

【 授权许可】

Unknown