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. However, only consecutive samples in a time series have been considered in the scientific literature for entropy computation applied to distress identification. 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 calmness and distress emotional states in EEG signals. Moreover, a number of curve-related features were also calculated to assess the signal dynamics across different temporal intervals.Complementary information among these variables was studied through stepwise regression and ten-fold cross-validation approaches. According to the results obtained, the multi-lag analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.

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