Xi'an University of Posts & Telecommunications, Shaanxi 710000, China.
J Healthc Eng. 2021 Dec 15;2021:9725762. doi: 10.1155/2021/9725762. eCollection 2021.
In order to improve the classification accuracy and reliability of emotional state assessment and provide support and help for music therapy, this paper proposes an EEG analysis method based on wavelet transform under the stimulation of music perception. Using the data from the multichannel standard emotion database (DEAP), , , and rhythms are extracted in frontal (F3 and F4), temporal (T7 and T8), and central (C3 and C4) channels with wavelet transform. EMD is performed on the extracted EEG rhythm to obtain intrinsic mode function (IMF) components, and then, the average energy and amplitude difference eigenvalues of IMF components of EEG rhythm waves are further extracted, that is, each rhythm wave contains three average energy characteristics and two amplitude difference eigenvalues so as to fully extract EEG feature information. Finally, emotional state evaluation is realized based on a support vector machine classifier. The results show that the correct rate between no emotion, positive emotion, and negative emotion can reach more than 90%. Among the pairwise classification problems among the four emotions selected, the classification accuracy obtained by this EEG feature extraction method is higher than that obtained by general feature extraction methods, which can reach about 70%. Changes in EEG wave power were closely correlated with the polarity and intensity of emotion; wave power varied significantly between "happiness and fear," "pleasure and fear," and "fear and sadness." It has a good application prospect in both psychological and physiological research of emotional perception and practical application.
为了提高情绪状态评估的分类准确性和可靠性，并为音乐治疗提供支持和帮助，本文提出了一种基于音乐感知刺激下的小波变换的 EEG 分析方法。利用多通道标准情绪数据库（DEAP）中的数据，在额（F3 和 F4）、颞（T7 和 T8）和中央（C3 和 C4）通道中使用小波变换提取节律。对提取的 EEG 节律进行 EMD 以获得固有模态函数（IMF）分量，然后进一步提取 EEG 节律波的 IMF 分量的平均能量和幅度差特征值，即每个节律波包含三个平均能量特征和两个幅度差特征值，从而充分提取 EEG 特征信息。最后，基于支持向量机分类器实现情绪状态评估。结果表明，无情绪、正情绪和负情绪之间的正确率可达 90%以上。在所选择的四种情绪的两两分类问题中，这种 EEG 特征提取方法获得的分类准确性高于一般特征提取方法，可达 70%左右。EEG 波功率的变化与情绪的极性和强度密切相关；在“快乐与恐惧”、“愉悦与恐惧”和“恐惧与悲伤”之间，波功率变化显著。它在情绪感知的心理和生理研究以及实际应用中都具有良好的应用前景。
