The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and suffer from complex search scenarios where user interactions are absent or biased.

Recently, the advances in portable and high-precision brain-computer interface (BCI) devices have shown the possibility to monitor user’s brain activities during search process. Brain signals can directly reflect user’s psychological responses to search results and thus it can act as additional and unbiased RF signals. To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based RF with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking. The experimental results on the user study dataset show that incorporating brain signals leads to significant performance improvement in our RF framework. Besides, we observe that brain signals perform particularly well in several hard search scenarios, especially when implicit signals as feedback are missing or noisy. This reveals when and how to exploit brain signals in the context of RF.

相关性反馈（RF）过程依赖于反馈文档的准确、实时的相关性估计来提高检索性能。由于收集显式相关性注释会给用户带来额外的负担，因此广泛的研究已经探索使用伪相关性信号和隐式反馈信号作为替代品。然而，此类信号是相关性的间接指标，并且会受到用户交互缺失或有偏差的复杂搜索场景的影响。

最近，便携式高精度脑机接口（BCI）设备的进步已经显示出在搜索过程中监测用户大脑活动的可能性。大脑信号可以直接反映用户对搜索结果的心理反应，因此可以充当附加且公正的射频信号。为了探索 RF 背景下大脑信号的有效性，我们提出了一种新颖的 RF 框架，它将基于 BCI 的 RF 与伪相关信号和隐式信号相结合，以提高文档重新排序的性能。用户研究数据集的实验结果表明，合并大脑信号可以显着提高我们的 RF 框架的性能。此外，我们观察到大脑信号在几种硬搜索场景中表现得特别好，特别是当作为反馈的隐式信号丢失或有噪声时。这揭示了何时以及如何在射频背景下利用大脑信号。