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The article analyzes the effectiveness of selected electrodermal activity (EDA) signal features as indicators of cognitive load. The study involved 30 healthy participants performing tasks of varying cognitive load levels. Collected EDA data were statistically analyzed, comparing the effectiveness of twelve statistical signal measures in detecting stimulus-induced changes. Results indicated that amplitude-related measures—mean, median, maximum, and minimum—were most effective. It was also found that some signal features were highly correlated, suggesting the possibility of simplifying the analysis by choosing just one measure from each correlated pair. The results indicate that stronger emotional stimuli lead to more pronounced changes in electrodermal activity compared to stimuli with low emotional load. These findings may contribute to the standardization of EDA analysis in future research on cognitive load and emotional engagement.