Brain-Inspired Visual Attention Modeling Based on EEG for Intelligent Robotics

Abstract

Vision, as the primary perceptual mode for intelligent robots, plays a crucial role in various human-robot interaction (HRI) scenarios. In certain situations, it is essential to utilize the visual sensors to capture videos for humans, assisting them in tasks like exploration missions. However, the increasing amount of video information brings great challenges for data transmission and storage. Therefore, there is an urgent need to develop more efficient video compression strategies to address this challenge. When perceiving a video, humans tend to pay more attention to some specific clips, which may occupy a small part of the whole video content, but largely affect the perceptual quality. This human visual attention (VA) mechanism provides valuable inspiration for optimizing video compression methods for HRI scenarios. Therefore, we combine psychophysiological paradigms and machine learning methods to model human VA and introduce it into the bitrate allocation to fully utilize the limited resources. Specifically, we collect electroencephalographic (EEG) data when humans watch videos, constructing an EEG dataset reflecting VA. Based on the dataset, we propose a VA measurement model to determine the VA states of humans in their underlying brain responses. Then, a brain-inspired VA prediction model is established to obtain VA metrics directly from the videos. Finally, based on the VA metric, more bitrates are allocated to the clips that humans pay more attention to. The experimental results show that our proposed methods can accurately determine the humans' VA states and predict the VA metrics evoked by different video clips. Furthermore, the bitrate allocation method based on the VA metric can achieve better perceptual quality at low bitrates.