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Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals
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 interf...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5143956/ https://www.ncbi.nlm.nih.gov/pubmed/27929077 http://dx.doi.org/10.1038/srep38580 |
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author | Eugster, Manuel J. A. Ruotsalo, Tuukka Spapé, Michiel M. Barral, Oswald Ravaja, Niklas Jacucci, Giulio Kaski, Samuel |
author_facet | Eugster, Manuel J. A. Ruotsalo, Tuukka Spapé, Michiel M. Barral, Oswald Ravaja, Niklas Jacucci, Giulio Kaski, Samuel |
author_sort | Eugster, Manuel J. A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5143956 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-51439562016-12-16 Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals Eugster, Manuel J. A. Ruotsalo, Tuukka Spapé, Michiel M. Barral, Oswald Ravaja, Niklas Jacucci, Giulio Kaski, Samuel Sci Rep Article 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. Nature Publishing Group 2016-12-08 /pmc/articles/PMC5143956/ /pubmed/27929077 http://dx.doi.org/10.1038/srep38580 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Eugster, Manuel J. A. Ruotsalo, Tuukka Spapé, Michiel M. Barral, Oswald Ravaja, Niklas Jacucci, Giulio Kaski, Samuel Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals |
title | Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals |
title_full | Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals |
title_fullStr | Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals |
title_full_unstemmed | Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals |
title_short | Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals |
title_sort | natural brain-information interfaces: recommending information by relevance inferred from human brain signals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5143956/ https://www.ncbi.nlm.nih.gov/pubmed/27929077 http://dx.doi.org/10.1038/srep38580 |
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