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Abstract：
A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing four prior-labeled emotion-inducing video clips from a publically available, validated database. Besides emotion category labels, the video clips also came with arousal scores. Our subjects were also asked to provide their own emotion labels. In total 30 subjects with age range 19-70 years participated in our study. Rather than relying on predefined frequency bands, we estimate multivariate sample entropy over multiple data-driven scales using the multivariate empirical mode decomposition (MEMD) technique and show that in this way we can discriminate between five self-reported emotions (p < 0.05). These results could not be obtained by analyzing the relation between arousal scores and video clips, signal complexity and arousal scores, and self-reported emotions and traditional power spectral densities and their hemispheric asymmetries in the theta, alpha, beta, and gamma frequency bands. This shows that multivariate, multiscale sample entropy is a promising technique to discriminate multiple emotional states from EEG recordings.
摘要：
将信号复杂度的多变量样本熵度量应用于当受试者观看来自公开可用的四个先前标记的情绪诱导视频剪辑时记录的EEG数据，已验证的数据库。除了情感类别标签，视频剪辑还带有唤醒分数。我们的受试者还被要求提供他们自己的情感标签。共有30名年龄范围为19-70岁的受试者参与了我们的研究。而不是依赖于预定义的频带，我们使用多元经验模式分解(MEMD)技术在多个数据驱动的尺度上估计多元样本熵,并表明通过这种方式我们可以区分五种自我报告的情绪(p<0.05).这些结果不能通过分析唤醒分数和视频剪辑之间的关系来获得，信号复杂性和唤醒分数，自我报告的情绪和传统的功率谱密度以及它们在theta上的半球不对称性，阿尔法，beta,和伽马频带。这表明多变量，多尺度样本熵是区分脑电图记录中多种情绪状态的一种有前途的技术。
