Abstract Recent advances in lifelogging, mainly due to fast development of wearable cameras, made it possible to continuously capture moments from our life from a first-person point of view. Extracting and re-experimenting moments illustrated by autobiographic images is of special interest in order to stimulate episodic memory of patients with neurodegenerative diseases (Alzheimer, mild cognitive impairment, etc.). Using a wearable camera, it is possible to generate a huge amount of images captured on a daily basis (around 2 , 000 images per day on a 30 s time-lapse mode). Since not all images obtained are valuable and semantically rich, there is a need for efficient and scalable techniques to separate the wheat from the chaff , that is. to extract egocentric images that are semantically rich enough and not redundant in order to use them for memory stimulation. By using state-of-the-art retrieval systems based on convolutional neural network features obtained from these rich, filtered egocentric images, we show how to cope with those requirements and apply the filtered images within a memory stimulation program specially developed to improve memory of patients with Mild Cognitive Impairment.