A Novel Graph Regularized Sparse Linear Discriminant Analysis Model for EEG Emotion Recognition

Abstract

In this paper, a novel regression model, called graph regularized sparse linear discriminant analysis (GraphSLDA), is proposed to deal with EEG emotion recognition problem. GraphSLDA extends the conventional linear discriminant analysis (LDA) method by imposing a graph regularization and a sparse regularization on the transform matrix of LDA, such that it is able to simultaneously cope with sparse transform matrix learning while preserve the intrinsic manifold of the data samples. To cope with the EEG emotion recognition, we extract a set of frequency based EEG features to training the GraphSLDA model and also use it as EEG emotion classifier for testing EEG signals, in which we divide the raw EEG signals into five frequency bands, i.e., $\delta$, $\theta$, $\alpha$, $\beta$, and $\gamma$. To evaluate the proposed GraphSLDA model, we conduct experiments on the SEED database. The experimental results show that the proposed algorithm GraphSLDA is superior to the classic baselines.