  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.
