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

TL;DR

This paper presents a brain-information interface that infers user relevance directly from EEG signals while reading, enabling automatic, implicit content recommendation from large document collections like Wikipedia.

Contribution

It introduces a novel brain-based relevance prediction method that models user interests from EEG signals during reading, facilitating implicit information retrieval without explicit interaction.

Findings

EEG signals can predict word relevance during reading.

User interests can be modeled from brain signals for document retrieval.

The approach successfully retrieves relevant Wikipedia articles based on inferred relevance.

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 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.