One of the foundational goals of Information Retrieval (IR) is to satisfy searchers' Information Needs (IN). Understanding how INs physically manifest has long been a complex and elusive process. However, recent studies utilising Electroencephalography (EEG) data have provided real-time insights into the neural processes associated with INs. Unfortunately, they have yet to demonstrate how this insight can practically benefit the search experience. As such, within this study, we explore the ability to predict the realisation of IN within EEG data across 14 subjects whilst partaking in a Question-Answering (Q/A) task. Furthermore, we investigate the combinations of EEG features that yield optimal predictive performance, as well as identify regions within the Q/A queries where a subject's realisation of IN is more pronounced. The findings from this work demonstrate that EEG data is sufficient for the real-time prediction of the realisation of an IN across all subjects with an accuracy of 73.5% (SD 2.6%) and on a per-subject basis with an accuracy of 90.1% (SD 22.1%). This work helps to close the gap by bridging theoretical neuroscientific advancements with tangible improvements in information retrieval practices, paving the way for real-time prediction of the realisation of IN.

翻译：信息检索（IR）的基本目标之一是满足搜索者的信息需求（IN）。理解信息需求如何在生理层面显现，长期以来一直是一个复杂且难以捉摸的过程。然而，近期利用脑电图（EEG）数据的研究为与信息需求相关的神经过程提供了实时洞察。遗憾的是，这些研究尚未证明这种洞察如何实际改善搜索体验。因此，在本研究中，我们探索了利用14名受试者在参与问答（Q/A）任务时的脑电图数据来预测信息需求实现的能力。此外，我们研究了能产生最佳预测性能的脑电图特征组合，并识别了问答查询中受试者信息需求实现更为显著的区域。本研究的结果表明，脑电图数据足以实时预测所有受试者的信息需求实现，准确率达到73.5%（标准差2.6%）；在个体受试者层面，准确率可达90.1%（标准差22.1%）。这项工作通过将理论神经科学的进展与信息检索实践的具体改进相结合，有助于弥合现有差距，为信息需求实现的实时预测铺平了道路。