* Departamento de Sistemas Informáticos, Escuela Politécnica de Cuenca, Universidad de Castilla-La Mancha, 16071-Cuenca, Spain.
† Departamento de Sistemas Informáticos, Escuela de Ingenieros Industriales de Albacete, Universidad de Castilla-La Mancha, 02071-Albacete, Spain.
Int J Neural Syst. 2019 Mar;29(2):1850038. doi: 10.1142/S0129065718500387. Epub 2018 Aug 24.
Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet. Hence, in this work two nonlinear indices for quantifying regularity and predictability of time series from several time scales are studied for the first time to discern between visually elicited emotional states of calmness and negative stress. The obtained results have revealed the maximum discriminant ability of 86.35% for the second time scale, thus suggesting that brain dynamics triggered by negative stress can be more clearly assessed after removal of some fast temporal oscillations. Moreover, both metrics have also been able to report complementary information for some brain areas.
自动识别负面压力是一个尚未解决的挑战，近年来受到了极大关注。许多研究都分析了脑电图（EEG）记录，以获得有关大脑如何对短期和长期压力刺激做出反应的新见解。尽管其中大多数只考虑了线性方法，但大脑的异质性和复杂性最近促使越来越多地使用非线性指标。尽管如此，脑电图记录中反映的大脑动力学通常表现出多尺度性质，而且尚未开展研究这一方面的研究。因此，在这项工作中，首次研究了两个用于从多个时间尺度量化时间序列的规则性和可预测性的非线性指标，以区分视觉诱发的平静和负面压力情绪状态。获得的结果表明，第二时间尺度的最大判别能力为 86.35%，这表明在去除一些快速时间振荡后，负面压力引发的大脑动力学可以更清晰地评估。此外，这两个指标都能够为一些脑区提供补充信息。
