人类情感识别仍然是一个具有挑战性和突出的问题,位于不同领域的汇合处,比如脑机接口,神经科学,和心理学。本研究利用EEG数据集来调查人类的情绪,提出了新的发现和基于脑电图的情绪检测的改进方法。Tsallis熵特征,为2、3和4的q值计算,从信号频带中提取,包括θ-θ(4-7Hz),α-α(8-15Hz),β-β(16-31Hz),gamma-γ(32-55Hz),和整体频率范围(0-75Hz)。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。在这项研究中,在Tsallis参数q=3的伽玛频率范围内,最佳平均准确率为79%,F评分为0.81.此外,观察到最高的准确性和F评分,分别为84%和0.87.值得注意的是,在情绪研究的背景下,与后半球和右半球相比,前半球和左半球表现优异。研究结果表明,该方法表现出更好的性能,使其成为现有技术的极具竞争力的替代品。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有价值的见解。
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.