  Finding relevant information from large document collections such as the
World Wide Web is a common task in our daily lives. Estimation of a user's
interest or search intention is necessary to recommend and retrieve relevant
information from these collections. We introduce a brain-information interface
used for recommending information by relevance inferred directly from brain
signals. In experiments, participants were asked to read Wikipedia documents
about a selection of topics while their EEG was recorded. Based on the
prediction of word relevance, the individual's search intent was modeled and
successfully used for retrieving new, relevant documents from the whole English
Wikipedia corpus. The results show that the users' interests towards digital
content can be modeled from the brain signals evoked by reading. The introduced
brain-relevance paradigm enables the recommendation of information without any
explicit user interaction, and may be applied across diverse
information-intensive applications.
