Research Area:  Machine Learning

Abstract:

EEG-based emotion recognition has attracted substantial attention from researchers due to its extensive application prospects, and substantial progress has been made in feature extraction and classification modelling from EEG data. However, insufficient high-quality training data are available for building EEG-based emotion recognition models via machine learning or deep learning methods. The artificial generation of high-quality data is an effective approach for overcoming this problem. In this paper, a multi-generator conditional Wasserstein GAN method is proposed for the generation of high-quality artificial that covers a more comprehensive distribution of real data through the use of various generators. Experimental results demonstrate that the artificial data that are generated by the proposed model can effectively improve the performance of emotion classification models that are based on EEG.

Keywords:  
EEG Data Augmentation
Emotion Recognition
Multiple Generator Conditional Wasserstein Gan
Deep Learning
Machine Learning

Author(s) Name:  Aiming Zhang, Lei Su, Yin Zhang, Yunfa Fu, Liping Wu & Shengjin Liang

Publisher name:  Springer

DOI:  10.1007/s40747-021-00336-7

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s40747-021-00336-7