Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

Manuel Eugster, Tuukka Ruotsalo, Michiel Spape, Oswald Barral, Niklas Ravaja, Giulio Jacucci, Samuel Kaski

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
161 Downloads (Pure)

Abstract

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 toward 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.
Original languageEnglish
Article number38580
Pages (from-to)1-10
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 9 Nov 2016
MoE publication typeA1 Journal article-refereed

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