Overview of the EEG Pilot Subtask at MediaEval 2021: Predicting Media Memorability

TL;DR

This paper presents a pilot subtask at MediaEval 2021 focused on using EEG neural signals to predict video memorability, providing a dataset and demonstrating the potential of neural data in multimedia memorability prediction.

Contribution

It introduces a new EEG dataset for memorability prediction and explores how neural signals can be utilized without prior domain knowledge to enhance multimedia analysis.

Findings

EEG features can be used to predict video memorability.

The dataset supports development of novel machine learning strategies.

Potential for interdisciplinary approaches combining EEG and computer vision.

Abstract

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.