Where Is My Mind (looking at)? Predicting Visual Attention from Brain Activity

TL;DR

This paper demonstrates that visual attention can be predicted from EEG brain signals with accuracy comparable to image-based methods, advancing non-invasive attention estimation techniques.

Contribution

It introduces models that predict visual attention from EEG signals, showing results comparable to traditional image-based saliency predictions.

Findings

EEG signals can effectively predict visual attention.

Models achieve comparable accuracy to image-based methods.

Dataset and code are publicly available for further research.

Abstract

Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, artificial intelligence and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. For this purpose, a set of signals has been recorded and different models have been developed to study the relationship between visual attention and brain activity. The results are encouraging and comparable with other approaches estimating attention with other modalities. The codes and dataset considered in this paper have been made available at \url{https://figshare.com/s/3e353bd1c621962888ad} to promote research in the field.