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

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Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals. / Eugster, Manuel; Ruotsalo, Tuukka; Spape, Michiel; Barral, Oswald; Ravaja, Niklas; Jacucci, Giulio; Kaski, Samuel.

In: Scientific Reports, Vol. 6, 38580 , 09.11.2016, p. 1-10.

Research output: Contribution to journalArticleScientificpeer-review

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@article{1ac4ea63e9c145f591d9e734cb2e93d1,
title = "Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals",
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.",
author = "Manuel Eugster and Tuukka Ruotsalo and Michiel Spape and Oswald Barral and Niklas Ravaja and Giulio Jacucci and Samuel Kaski",
year = "2016",
month = "11",
day = "9",
doi = "10.1038/srep38580",
language = "English",
volume = "6",
pages = "1--10",
journal = "Scientific Reports",
issn = "2045-2322",

}

RIS - Download

TY - JOUR

T1 - Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

AU - Eugster, Manuel

AU - Ruotsalo, Tuukka

AU - Spape, Michiel

AU - Barral, Oswald

AU - Ravaja, Niklas

AU - Jacucci, Giulio

AU - Kaski, Samuel

PY - 2016/11/9

Y1 - 2016/11/9

N2 - 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.

AB - 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.

U2 - 10.1038/srep38580

DO - 10.1038/srep38580

M3 - Article

VL - 6

SP - 1

EP - 10

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 38580

ER -

ID: 9815094