Data_Sheet_1_Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography.docx

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L1/2 norm framework for feature extraction, and uses L2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.

近年来，基于脑电图（electroencephalogram, EEG）数据的情感计算领域受到了日益广泛的关注。作为经典的脑电图特征提取模型，格兰杰因果分析（Granger causality analysis）已被广泛应用于情感分类模型中：此类模型通过计算脑电图传感器之间的因果关系构建脑网络，并筛选关键脑电图特征。传统的脑电图格兰杰因果分析采用L2范数从数据中提取特征，因此其结果极易受脑电图伪迹的干扰。
近期，已有研究者提出基于最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）以及L1/2范数的格兰杰因果分析模型以解决上述问题。然而，传统的稀疏格兰杰因果分析模型假设各传感器之间的连接具有相同的先验概率。
本文证明，若将各传感器的脑电图数据间的相关性作为先验知识融入格兰杰因果网络，则可提升稀疏格兰杰因果模型的脑电图特征筛选能力与情感分类性能。基于这一思路，我们提出一种新型情感计算模型——基于传感器相关性的稀疏格兰杰因果分析模型（SC-SGA）。
SC-SGA将传感器间的相关性作为先验知识，融入基于L1/2范数框架的格兰杰因果分析以完成特征提取，并采用L2范数逻辑回归作为情感分类算法。我们通过两个真实的脑电图情感数据集开展了实验，实验结果表明，SC-SGA模型的情感分类准确率较现有模型提升了2.46%~21.81%。