摘要：

人的情感在发生变化时, 不同通道间脑电图(EEG) 信号会交互作用, 且不同频段交互特征存在分脑区特性。为提取全脑交互性特征和充分捕获特征在不同脑区频段间依赖性, 提出主对角线非0的格兰杰因果(GC)特征提取方法和分脑区分频段的Transformer模型。首先, 针对计算自身因果度量值时GC值为0的问题, 通过改进GC算法, 提取出EEG信号各通道非0的自身因果信息。然后, 针对常用情感识别模型总是关注局部特性, 缺乏全局视野的问题, 根据不同频段下同脑区间存在关联的特点, 对因果特征进行脑区频段划分, 使用脑区频段Transformer模型将特征进行不同脑区不同频段特征间的依赖性和贡献捕获。在TYUT3.0数据集上的实验结果表明, 在使用提出的脑区频段Transformer模型分类识别时, 主对角线非0 GC矩阵相比于常用GC矩阵, 平均识别准确率提升了约1.59百分点, 说明了所提出特征的优越性; 在使用提出的主对角线非0 GC矩阵作为特征时, 提出的脑区频段Transformer模型平均准确率达到94.50%, 较已有的模型平均识别准确率提升了1.89百分点, 说明了脑区频段划分特征依赖性及全局融合思路的有效性。

关键词: 格兰杰因果, 脑区, 频段, Transformer模型, 脑电图情感识别

Abstract:

When human emotions change, the Electroencephalogram (EEG) signals across different channels interact, and distinct brain regions exhibit characteristic interaction features in different frequency bands. To extract global interactive features and comprehensively capture the interdependence of features across various brain regions and frequency bands, this study proposes a principal diagonal nonzero Granger Causality (GC) feature extraction method and a region-specific frequency division Transformer model. First, by addressing the issue of GC values being zero when calculating self-causality measures, this study enhances the Granger causality algorithm to extract nonzero self-causal information for each channel of EEG signals. Subsequently, to overcome the common limitation of emotion recognition models that focus on local characteristics and lack a global perspective, this study leverages the observed associations between different brain regions within the same frequency band. The method partitions causality features into brain frequencies and employs a brain-frequency division Transformer model to capture the interdependence and contribution of features across different brain regions and frequency bands. Experimental results on the TYUT3.0 dataset demonstrate that when using the proposed region-specific frequency division Transformer model for classification, the principal diagonal non-zero GC matrix, compared to commonly used GC matrices, achieves an average recognition accuracy improvement of approximately 1.59 percentage points. This suggests the superiority of the proposed features. When using the principal diagonal non-zero GC matrix as features, the proposed region-specific frequency division Transformer model achieves an average accuracy of 94.50%, surpassing existing models by more than 1.89 percentage points on average recognition accuracy. This indicates the effectiveness of the approach in globally integrating features with dependencies under brain region-specific frequency divisions.

Key words: Granger Causality(GC), brain regions, frequency bands, Transformer model, Electroencephalogram(EEG) emotion recognition