Physiological signals present fluctuations that can be assessed from their temporal structure, also termed complexity. The complexity of a physiological signal is usually quantified using entropy estimators, such as Sample Entropy. Recent studies have shown a loss of force signal complexity with the development of neuromuscular fatigue. However, these studies did not consider the stationarity of the force signals which is an important prerequisite of Sample Entropy measurements. Here, we investigated the effect of the potential nonstationarity of force signals on the kinetics of neuromuscular fatigue-induced change in force signal's complexity. Eleven men performed submaximal intermittent isometric contractions of knee extensors until exhaustion. Neuromuscular fatigue was assessed from changes in voluntary and electrically evoked contractions. Sample Entropy values were computed from submaximal force signals throughout the fatiguing task. The Dickey-Fuller test was used to statistically investigate the stationarity of force signals and the Empirical Mode Decomposition was applied to detrend these signals. Maximal voluntary force, central voluntary activation and muscle twitch decreased throughout the task (all ), indicating the development of global, central and peripheral fatigue, respectively. We found an increase in Sample Entropy with fatigue ( p = 0.024 ) when not considering the nonstationarity of force signals (i.e., 43% of nonstationary signals). After applying the Empirical Mode Decomposition, we found a decrease in Sample Entropy with fatigue ( p = 0.002 ). These findings confirm the presence of nonstationarity in force signals during submaximal isometric contractions which influences the kinetics of Sample Entropy with neuromuscular fatigue.