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.