EEG-based emotion classification using spiking neural networks

Yuling Luo, Qiang Fu, Juntao Xie, Yunbai Qin, Guopei Wu, Junxiu Liu, frank jiang, Yi Cao, Xuemei Ding

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)46007-46016
Number of pages10
JournalIEEE Access
Publication statusPublished - 16 Mar 2020

Keywords / Materials (for Non-textual outputs)

  • emotion classification
  • spiking neural network
  • EEG signal


Dive into the research topics of 'EEG-based emotion classification using spiking neural networks'. Together they form a unique fingerprint.

Cite this