Spontaneous mind wandering analysis is essential in understanding the mental state of an individual and could aid in improving performance and productivity. This paper proposes a framework to analyze the Electrodermal Activity (EDA) signals to detect mind wandering based on empirical mode decomposed entropy features. For this analysis, EDA signals are considered from a publicly available database, and are preprocessed for noise removal. These preprocessed signals are decomposed using Empirical Mode Decomposition to three Intrinsic Mode Functions (IMFs). The significant IMF is determined by using statistical and entropy analysis such as approximate, sample, singular value decomposition entropies. Results indicate that the IMF of lower frequency showed higher mean percentage deviation. The F-statistic of singular value decomposition entropy is significant in differentiating the mind wandering characteristics of an individual. Thus, the proposed framework could be employed in real time for monitoring of mind awareness.