用脑电与眼动数据识别搜索意图，支持跨用户预测
Implicit Search Intent Recognition using EEG and Eye Tracking: Novel Dataset and Cross-User Prediction
- 基于脑电与眼动数据，实现无须用户主动输入的意图识别
- 跨用户预测准确率达84.5%，接近同用户水平（85.5%）
- 首个公开数据集，支持自然终止的搜索任务设计
为使机器在复杂视觉搜索任务中有效辅助人类，必须区分用户是随意浏览场景（导航意图）还是寻找目标物体（信息意图）。以往研究尝试结合脑电图（EEG）和眼动追踪来隐式识别此类意图，无需用户主动输入。但现有方法存在两大局限：一是信息意图条件下采用固定搜索时长，与真实搜索中找到目标即停止的自然行为不符；二是依赖目标用户的标注训练数据，难以在实际场景中应用。本文提出首个公开的脑电与眼动数据集，用于导航与信息意图识别，且由用户自主决定搜索结束时间。我们还首次实现跨用户搜索意图预测，在留一用户评估中达到84.5%准确率，与同用户预测精度（85.5%）相当，具备更强实用性。
原文摘要 · Abstract (English)
For machines to effectively assist humans in challenging visual search tasks, they must differentiate whether a human is simply glancing into a scene (navigational intent) or searching for a target object (informational intent). Previous research proposed combining electroencephalography (EEG) and eye-tracking measurements to recognize such search intents implicitly, i.e., without explicit user input. However, the applicability of these approaches to real-world scenarios suffers from two key limitations. First, previous work used fixed search times in the informational intent condition -- a stark contrast to visual search, which naturally terminates when the target is found. Second, methods incorporating EEG measurements addressed prediction scenarios that require ground truth training data from the target user, which is impractical in many use cases. We address these limitations by making the first publicly available EEG and eye-tracking dataset for navigational vs. informational intent recognition, where the user determines search times. We present the first method for cross-user prediction of search intents from EEG and eye-tracking recordings and reach 84.5% accuracy in leave-one-user-out evaluations -- comparable to within-user prediction accuracy (85.5%) but offering much greater flexibility
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