Autobiographical memory (AM) is a complex process that relies on distributed and dynamic brain networks. A crucial stage is the reconstruction phase, when individuals reexperience and mentally recreate an event, imagining it with vivid sensory, contextual, and emotional details. Despite the richness and complexity of this process, the neural characterization of AM reconstruction has mainly relied on classical measures of connectivity and spectral power, leaving frameworks such as Information Theory unexplored in this context. In this work, we propose to investigate AM reconstruction using information-theoretic quantifiers (Shannon entropy and Statistical Complexity) as measures to characterize different types of memories varying in age, detail, importance, and emotional valence, among other features. Our aim is to apply non-traditional techniques to uncover neural patterns unexplored with standard approaches to identify distinctive markers linked explicitly to the mental reconstruction of autobiographical events. The use of these measures may provide novel markers, offering new insights into how AMs are recalled and represented. Given the inherent nature of AM mental elaboration, we focus on occipital regions, seeking distinctive markers of visual and reconstructive processing. We analyze 8-second-long EEG time series (30 channels, 256 Hz sampling rate) recorded from 33 participants during an AM reconstruction task.