Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals
This enables information recommendation without explicit user interaction, potentially benefiting users in information-intensive applications, though it appears incremental as it builds on existing brain-computer interface concepts.
The paper tackled the problem of recommending information by directly inferring user interest from brain signals, achieving successful retrieval of relevant documents from the entire English Wikipedia corpus based on EEG recordings during reading.
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.