Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography - Chen, Dongwei; Miao, Rui; Deng, Zhaoyong; Han, Na; Deng, Chunjian (Frontiers in Computational Neuroscience, 2021)
In recent years, EEG-based sentiment computing has received more and more attention from researchers. Granger causality analysis as a classic feature extraction model has been widely used in sentiment classification models. The model constructs a brain network by calculating the causality relationship between EEG sensors and selects EEG features. Because the original Granger causality analysis uses L2 norm as the loss function and does not perform sparseness. This can cause the results to be affected by noise in the EEG data. Therefore, the researchers put forward Granger causality analysis based on LASSO and Granger causality analysis model based on L1/2 norm to solve the problem of noise. The existing sparse Granger causality analysis model assumes that the connection between each sensor has the same prior probability. However, this article shows that if the correlation of EEG data between each sensor can be added to the Granger causality network as prior knowledge. It can enhance the causal selection ability of the existing sparse Granger causality model, thereby enhancing the model’s EEG feature selection ability. Finally, it can effectively improve the emotion recognition ability of the emotion classifier based on the sparse Granger causality model. In this situation, this paper proposes a new emotional computing model, named Sparse Granger causality analysis model based on sensors correlation(SC-SGA), which uses the correlation between sensors as prior knowledge and combines L1/2-based sparse Granger causality analysis for feature extraction. Finally, the model uses L2 norm logistic regression as the classification algorithm. The experimental results show that the sparse Granger classification model based on sensor similarity proposed in this paper is better than the existing models, and can well identify the emotional state of subjects.