The result of the emotional state induced by music may provide theoretical support and help for assisted music therapy. The key to assessing the state of emotion is feature extraction of the emotional electroencephalogram (EEG). In this paper, we study the performance optimization of the feature extraction algorithm. A public multimodal database for emotion analysis using physiological signals (DEAP) proposed by Koelstra et al. was applied. Eight kinds of positive and negative emotions were extracted from the dataset, representing the data of fourteen channels from the different regions of brain. Based on wavelet transform, δ, θ, α and β rhythms were extracted. This paper analyzed and compared the performances of three kinds of EEG features for emotion classification, namely wavelet features (wavelet coefficients energy and wavelet entropy), approximate entropy and Hurst exponent. On this basis, an EEG feature fusion algorithm based on principal component analysis (PCA) was proposed. The principal component with a cumulative contribution rate more than 85% was retained, and the parameters which greatly varied in characteristic root were selected. The support vector machine was used to assess the state of emotion. The results showed that the average accuracy rates of emotional classification with wavelet features, approximate entropy and Hurst exponent were respectively 73.15%, 50.00% and 45.54%. By combining these three methods, the features fused with PCA possessed an accuracy of about 85%. The obtained classification accuracy by using the proposed fusion algorithm based on PCA was improved at least 12% than that by using single feature, providing assistance for emotional EEG feature extraction and music therapy.
音乐诱发下的情感状态评估结果可为辅助音乐治疗提供理论支持与帮助。情感状态评估的关键是情感脑电的特征提取，故本文针对情感脑电特征提取算法的性能优化问题开展研究。采用 Koelstra 等提出的分析人类情绪状态的多模态标准数据库 DEAP，提取 8 种正负情绪代表各个脑区的 14 个通道脑电数据，基于小波分解重构 δ、θ、α、β 四种节律波；在分析比较小波特征（小波系数能量和小波熵）、近似熵和 Hurst 指数三种脑电特征情感识别效果的基础上，提出一种基于主成分分析（PCA）融合小波特征、近似熵和 Hurst 指数的脑电特征提取算法。本算法保留累积贡献率大于 85% 的主成分，并选择特征根差异较大的特征参数，基于支持向量机实现情感状态评估。结果表明，使用单一小波特征（小波系数能量和小波熵）、近似熵和 Hurst 指数特征量，情感识别的正确率均值分别是 73.15%、50.00% 和 45.54%，而改进算法识别准确率均值在 85% 左右。基于改进算法情感识别的分类准确率比传统方法至少能提升 12%，可为情感脑电特征提取以及辅助音乐治疗提供帮助。.
Keywords: Hurst exponent; approximate entropy; feature fusion; musical emotion; principal component analysis; wavelet transform.