Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain–computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4–7 Hz), alpha-α (8–15 Hz), beta-β (16–31 Hz), gamma-γ (32–55 Hz), and the overall frequency range (0–75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.

中文翻译：

---

使用 Tsallis 熵特征和 KNN 分类器从多通道 EEG 子带进行跨主题情绪识别

人类情感识别仍然是一个具有挑战性和突出的问题，它位于脑机接口、神经科学和心理学等不同领域的融合中。这项研究利用脑电图数据集来研究人类情绪，提出了新颖的发现和基于脑电图的情绪检测的改进方法。针对 q 值 2、3 和 4 计算的 Tsallis 熵特征是从信号频带中提取的，包括 theta-θ (4–7 Hz)、alpha-α (8–15 Hz)、beta-β (16–31 Hz)、gamma-γ (32–55 Hz) 和整个频率范围 (0–75 Hz)。这些 Tsallis 熵特征用于训练和测试 KNN 分类器，旨在准确识别两种情绪状态：积极和消极。在本研究中，在 Tsallis 参数 q = 3 的伽玛频率范围内实现了 79% 的最佳平均准确度和 0.81 的 F 分数。此外，观察到了 84% 和 0.87 的最高准确度和 F 分数。值得注意的是，在情绪研究中，前半球和左半球的表现优于后半球和右半球。研究结果表明，所提出的方法表现出增强的性能，使其成为现有技术的极具竞争力的替代方案。此外，我们确定并讨论了所提出方法的缺点，为潜在的改进途径提供了宝贵的见解。