Overview of the EEG Pilot Subtask at MediaEval 2021: Predicting Media Memorability
This work provides a resource for researchers interested in combining EEG with computer vision for memorability prediction, but it is incremental as it builds on existing memorability tasks.
The paper introduced a pilot subtask and dataset using EEG features to predict video memorability, aiming to demonstrate the utility of neural signals without requiring prior domain knowledge.
The aim of the Memorability-EEG pilot subtask at MediaEval'2021 is to promote interest in the use of neural signals -- either alone or in combination with other data sources -- in the context of predicting video memorability by highlighting the utility of EEG data. The dataset created consists of pre-extracted features from EEG recordings of subjects while watching a subset of videos from Predicting Media Memorability subtask 1. This demonstration pilot gives interested researchers a sense of how neural signals can be used without any prior domain knowledge, and enables them to do so in a future memorability task. The dataset can be used to support the exploration of novel machine learning and processing strategies for predicting video memorability, while potentially increasing interdisciplinary interest in the subject of memorability, and opening the door to new combined EEG-computer vision approaches.