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The article delves into enhancing Relevance Feedback (RF) using brain signals alongside pseudo-relevance and implicit signals. Topics include the challenges of RF using traditional methods, the integration of brain signals to improve RF accuracy, and the framework's effectiveness in diverse search scenarios.

Recent technological advancements have led to a significant increase in the quantity and accessibility of videos. The decrease in video acquisition costs and the increase in memory capacity have made it possible to store large video collections in computer systems. To effectively exploit these collections, it is crucial to have tools that facilitate access and management. In this paper, we present a multimedia retrieval approach that prioritizes the user's needs by starting with a text-based query. The approach consists of two main parts: (i) a new multi-level and deep-semantic video classification indexing method, and (ii) a query expansion mechanism and relevance feedback system to improve the results based on the user's feedback. Our contribution is demonstrated through the implementation of the Deep-VISEN prototype and experiments on a collection of 2700 videos and 62838 images. The results show that our algorithm is effective and precise. [ABSTRACT FROM AUTHOR]