Visual Attention Measurement Based on Electroencephalogram Feature Learning

With the continuous development of multimedia technology, there is a growing demand for video applications. Among the huge number of videos, some video clips attract higher visual attention from viewers. Measuring this visual attention is not only crucial for evaluating the quality of experience (QoE) but also holds the potential for guiding video compression techniques. Therefore, there is an urgent need to propose an effective method to evaluate the changes of viewers' attention. To address this challenge, we employ electroencephalography (EEG) as a bridge to establish the relationship between video clips and visual attention. We introduce a visual attention measurement (VAM) method by combining EEG and machine learning approaches. Specifically, we design an EEG experiment to collect brain responses from viewers while watching various video clips, thus constructing a visual attention EEG dataset. Using these EEG signals, we propose a VAM network to identify whether viewers pay attention to the video clips. The experimental results show that our EEG experiment can capture the physiological signals related to visual attention. Moreover, the proposed VAM network can accurately determine viewers' attention levels to video clips. These results provide new insights into the evolution of human-oriented video communications.