School of Automation Engineering, Northeast Electric Power University, Jilin, China.
Luneng New Energy (Group) Co., Beijing, China.
Comput Intell Neurosci. 2019 Mar 6;2019:3191903. doi: 10.1155/2019/3191903. eCollection 2019.
Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.
音乐可以唤起各种情绪，这些情绪可能在脑电图（EEG）上表现出不同的信号。许多先前的研究都研究了音乐的特定方面与 EEG 信号特征之间的关联，包括引起的主观情绪。然而，尚无研究全面检查与音乐相关的 EEG 特征，并选择那些最有潜力区分情绪的特征。因此，本文进行了一系列实验，以确定由音乐引起的不同情绪（平静、喜悦、悲伤和愤怒）所产生的最具影响力的 EEG 特征。我们从每个 12 个电极位置中提取了 27 个维度的特征，然后使用基于相关性的特征选择方法来识别与原始特征最相关但冗余度最低的特征集。然后，使用几种分类器，包括支持向量机（SVM）、C4.5、LDA 和 BPNN，来测试原始和所选特征集的识别精度。最后，详细分析结果并清楚地显示所选特征集与人类情绪之间的关系。通过 10 次随机检查的分类结果，可以得出结论，当将 Pz 所选特征集用作分类人类情绪状态的关键特征集时，比其他特征更有效。
