EEGaze：结合 EEG 信号预测视觉搜索中的注意力分配
EEGaze: Multimodal Attention Prediction in Visual Search via EEG-Based Saliency Refinement
脑机接口方向可以看看：36 人、140 个场景的 EEG 加眼动公开数据集，还附带了首个用脑电信号修正显著性预测的模型。
研究团队发布了 EEGaze 140 数据集，收录 36 名参与者在 140 个自然场景下的同步 EEG 与眼动追踪记录，场景涵盖桌面图标搜索和杂乱车间找工具两类任务。同时提出 EEGazeNet，这是首个在视觉搜索中整合 EEG 信号的人类注意力预测方法。该方法用 EEG 引导的细化阶段，把图像显著性预测模型的输出适配到用户当前认知状态。在 EEGaze 140 上的评估显示，EEGazeNet 在所有基线和消融条件下表现一致更优。
EEGaze: Multimodal Attention Prediction in Visual Search via EEG-Based Saliency Refinement
Visual search is ubiquitous in daily life, from locating keys in a messy apartment to finding a tool in a cluttered workshop or an application icon on a desktop screen. While visual attention is to a large extent driven by image content, research in psychology indicates that user cognitive states have a significant influence on attention allocation. A promising way to access information about cognitive states is via Electroencephalography (EEG) recordings. Despite this potential, previous works in computational attention modeling have largely disregarded EEG, and related studies in psychology operate with abstract, simplistic stimuli, or even artificially guided search procedures. We bridge this gap with two contributions. First, we present EEGaze 140, the largest publicly available multimodal dataset of free visual search with realistic stimuli. The dataset contains synchronized EEG and eye tracking recordings from 36 participants across 140 naturalistic scenes, covering two scenarios: searching for desktop icons and finding tools in cluttered workshop scenes. Second, we introduce EEGazeNet, the first method for human attention prediction in visual search that integrates EEG signals. Our approach consists of a EEG guided refinement stage that adapts the output of a state of the art saliency prediction method to the user current cognitive state. In comprehensive evaluations on EEGaze 140, we show that EEGazeNet consistently outperforms all baselines and ablation conditions.
