Electrodermal activity (EDA) and heart rate variability (HRV) offer opportunities to grasp critical manifestations of the nervous autonomic system using low-intrusive sensing tools. A key question relies on the capacity to adequately process EDA and HRV signals to extract cognitive load markers, a multifaceted construct with intricate neural networks functioning, where emotions interfere with cognition. Here, 34 participants (20 males, 19.2 ± 1.3 years) were exposed to two-back mental tasking and watching emotionally charged images while recording EDA and HRV. HRV signals were processed using variable frequency complex demodulation (VFCDM) and wavelet packet transform (WPT) to provide high- and low-frequency (HF and LF) markers. Three methods were used to extract EDA indices: VFCDM (EDA_TVSYMP), WPT (EDA_WPT), and convex-optimization (EDA_CVX). Cognitive load and emotion epochs were distinguished by significant differences in NASA-TLX scores, mental fatigue, and stress, on the one hand; and by EDA_CVX and, remarkably, EDA_TVSYMP and HF-HRV_VFCDM on the other hand. A linear mixed-effects model and stepwise backward selection procedure showed that these two markers were main predictors of the NASA-TLX score (cognitive load). The individual perception of cognitive load was finally discriminated by k-means clustering, showing three profiles of autonomic responses relying, respectively, on EDA_TVSYMP, HF-HRV_VFCDM, or a mix of these two markers. The existence of EDA-, HRV-, and EDA/HRV-derived profiles might explain why previous attempts that have predominantly employed a single biosignal often remained unconclusive in evaluating the perceived cognitive load, thereby demonstrating the added value of the present approach to monitor mental-related workload in human operators.

Post-Traumatic Stress Disorder (PTSD) is consistently linked to deficits in executive functions, including attention, working memory, and inhibitory control, which impact clinical outcomes. This study examined these cognitive functions in military personnel with PTSD and its associations with clinical symptoms and objective sleep parameters. French active-duty and veteran service members with PTSD (n = 130) and healthy military controls (n = 65) completed computerized cognitive tasks: inhibition (Go/No-Go), sustained attention (0-Back), and working memory (2-Back). Sleep was monitored under ecological conditions using a polysomnographic headband. Clinical assessments included PTSD severity (PCL-5) and depression (BDI). Efficiency scores combining accuracy and speed were calculated for each task. Group comparisons, correlations, and multiple regressions were conducted. Executive impairment thresholds were derived from the controls to estimate prevalence in the PTSD group. PTSD participants showed lower performance across the three cognitive tasks. Using normative thresholds, 36.6% met criteria for executive impairment. In the PTSD group, clinical and sleep variables explained only a small proportion of the variance in cognitive performance (R² = 0.12), with REM sleep and PTSD severity showing the largest albeit modest contributions. In contrast, predictors explained a greater proportion of variance in controls (R² = 0.35), suggesting stronger predictive utility. Exploratory analyses indicated that medication burden was the strongest individual predictor of executive performance in PTSD. Executive dysfunction is frequent among military personnel with PTSD and only partially explained by standard clinical and sleep measures. Cognitive impairment likely reflects multiple interacting mechanisms not captured by standard assessments, underscoring the need for integrative and multimodal approaches.