  The tasks of legal case retrieval have received growing attention from the IR
community in the last decade. Relevance feedback techniques with implicit user
feedback (e.g., clicks) have been demonstrated to be effective in traditional
search tasks (e.g., Web search). In legal case retrieval, however, collecting
relevance feedback faces a couple of challenges that are difficult to resolve
under existing feedback paradigms. First, legal case retrieval is a complex
task as users often need to understand the relationship between legal cases in
detail to correctly judge their relevance. Traditional feedback signal such as
clicks is too coarse to use as they do not reflect any fine-grained relevance
information. Second, legal case documents are usually long, users often need
even tens of minutes to read and understand them. Simple behavior signal such
as clicks and eye-tracking fixations can hardly be useful when users almost
click and examine every part of the document. In this paper, we explore the
possibility of solving the feedback problem in legal case retrieval with brain
signal. Recent advances in brain signal processing have shown that human
emotional can be collected in fine grains through Brain-Machine Interfaces
(BMI) without interrupting the users in their tasks. Therefore, we propose a
framework for legal case retrieval that uses EEG signal to optimize retrieval
results. We collected and create a legal case retrieval dataset with users EEG
signal and propose several methods to extract effective EEG features for
relevance feedback. Our proposed features achieve a 71% accuracy for feedback
prediction with an SVM-RFE model, and our proposed ranking method that takes
into account the diverse needs of users can significantly improve user
satisfaction for legal case retrieval. Experiment results show that re-ranked
result list make user more satisfied.
