Li Jiawen, Feng Guanyuan, Ling Chen, Ren Ximing, Liu Xin, Zhang Shuang, Wang Leijun, Chen Yanmei, Zeng Xianxian, Chen Rongjun
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Hainan Provincial Key Laboratory of Sports and Health Promotion, Hainan Medical University, Haikou 571199, China.
Entropy (Basel). 2025 Jan 20;27(1):96. doi: 10.3390/e27010096.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). Then, such entropies are fused into a matrix to represent complex and dynamic characteristics of EEG, denoted as the Brain Rhythm Entropy Matrix (BREM). Next, Dynamic Time Warping (DTW), Mutual Information (MI), the Spearman Correlation Coefficient (SCC), and the Jaccard Similarity Coefficient (JSC) are applied to measure the similarity between the unknown testing BREM data and positive/negative emotional samples for classification. Experiments were conducted using the DEAP dataset, aiming to find a suitable scheme regarding similarity measures, time windows, and input numbers of channel data. The results reveal that DTW yields the best performance in similarity measures with a 5 s window. In addition, the single-channel input mode outperforms the single-region mode. The proposed method achieves 84.62% and 82.48% accuracy in arousal and valence classification tasks, respectively, indicating its effectiveness in reducing data dimensionality and computational complexity while maintaining an accuracy of over 80%. Such performances are remarkable when considering limited data resources as a concern, which opens possibilities for an innovative entropy fusion method that can help to design portable EEG-based emotion-aware devices for daily usage.
情绪识别是一种用于理解人类行为和心理状态的先进技术，在心理健康监测、人机交互和情感计算等方面有着广泛应用。基于脑电图（EEG）这种大脑自然产生的生物医学信号，本文提出了一种资源高效的多熵融合方法用于情绪状态分类。首先，应用离散小波变换（DWT）从EEG信号中提取五种脑电节律，即δ、θ、α、β和γ，随后获取多熵特征，包括谱熵（PSDE）、奇异谱熵（SSE）、样本熵（SE）、模糊熵（FE）、近似熵（AE）和排列熵（PE）。然后，将这些熵融合成一个矩阵来表示EEG的复杂动态特征，记为脑电节律熵矩阵（BREM）。接下来，应用动态时间规整（DTW）、互信息（MI）、斯皮尔曼相关系数（SCC）和杰卡德相似系数（JSC）来测量未知测试BREM数据与正负情绪样本之间的相似度以进行分类。使用DEAP数据集进行了实验，旨在找到关于相似性度量、时间窗口和通道数据输入数量的合适方案。结果表明，在相似性度量方面，DTW在5秒窗口下性能最佳。此外，单通道输入模式优于单区域模式。所提出的方法在唤醒度和效价分类任务中的准确率分别达到84.62%和82.48%，表明其在降低数据维度和计算复杂度的同时保持了超过80%的准确率，在考虑有限数据资源的情况下，这样的性能是显著的，这为一种创新的熵融合方法开辟了可能性，该方法有助于设计基于EEG的便携式日常情绪感知设备。
