Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.

中文翻译：

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用于遇险识别的 EEG 记录符号熵的多滞后分析

鉴于其对身心健康的长期负面影响，困境在发达社会是一个关键问题。研究这种情绪的兴趣在过去几年显着增加，在该研究领域，脑电图 (EEG) 信号优于其他生理变量。此外，大脑动力学的非平稳性推动了非线性度量的使用，例如大脑信号分析中的符号熵。因此，时滞对大脑模式评估的影响尚未经过测试。因此，在本研究中，首次在不同的时滞下计算了两个称为延迟排列熵和排列最小熵的排列熵，以区分来自 EEG 信号的平静和痛苦的情绪状态。而且，还计算了许多与曲线相关的特征，以评估不同时间间隔的大脑动态。通过顺序正向选择和 10 倍交叉验证方法研究这些变量之间的互补信息。根据获得的结果，多滞后熵分析能够揭示迄今为止尚未发现的新的重要见解，从而显着改善了从 EEG 记录中识别遇险的过程。