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

arXiv: https://arxiv.org/abs/1607.03502 · HTML: https://mlsanity.com/paper/1607.03502/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2016.

## Built on by (most cited first, 6 of 6)

- [Towards a Better Understanding Human Reading Comprehension with Brain Signals](https://mlsanity.com/paper/2108.01360/) (2021)
- [Relevance Feedback with Brain Signals](https://mlsanity.com/paper/2312.05669/) (2023)
- [Query Augmentation by Decoding Semantics from Brain Signals](https://mlsanity.com/paper/2402.15708/) (2024)
- [Neural Memory Decoding with EEG Data and Representation Learning](https://mlsanity.com/paper/2307.13181/) (2023)
- [On the Role of Activation Functions in EEG-To-Text Decoder](https://mlsanity.com/paper/2410.12572/) (2024)
- [Cross-Sensory Brain Passage Retrieval: Scaling Beyond Visual to Audio](https://mlsanity.com/paper/2601.14001/) (2026)

## Abstract (arXiv)

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.