For the first time, information retrieval is possible with the help of EEG interpreted with machine learning.
In a study conducted by the Helsinki Institute for Information Technology (HIIT) and the Centre of Excellence in Computational Inference (COIN), laboratory test subjects read the introductions of Wikipedia articles of their own choice. During the reading session, the test subjects’ EEG was recorded, and the readings were then used to model which key words the subjects found interesting.
‘The aim was to study if EEG can be used to identify the words relevant to a test subject, to predict a subject’s search intentions and to use this information to recommend new relevant and interesting documents to the subject. There are millions of documents in the English Wikipedia, so the recommendation accuracy was studied against this vast but controllable corpus’, says HIIT researcher Tuukka Ruotsalo.
Due to the noise in brain signals, machine learning was used for modelling, so that relevance and interest could be identified by learning the EEG responses. With the help of machine learning methods, it was possible to identify informative words, so they were also useful in the information retrieval application.
‘Information overload is a part of everyday life, and it is impossible to react to all the information we see. And according to this study, we don’t need to; EEG responses measured from brain signals can be used to predict a user’s reactions and intent’, tells HIIT researcher Manuel Eugster.
Based on the study, brain signals could be used to successfully predict other Wikipedia content that would interest the user.
‘Applying the method in real information retrieval situations seems promising based on the research findings. Nowadays, we use a lot of our working time searching for information, and there is much room in making knowledge work more effective, but practical applications still need more work. The main goal of this study was to show that this kind of new thing was possible in the first place’, tells Professor at the Department of Computer Science and Director of COIN Samuel Kaski.
‘It is possible that, in the future, EEG sensors can be worn comfortably. This way, machines could assist humans by automatically observing, marking and gathering relevant information by monitoring EEG responses’, adds Ruotsalo.
The study was carried out in cooperation by the Helsinki Institute for Information Technology (HIIT), which is jointly run by Aalto University and the University of Helsinki, and the Centre of Excellence in Computational Inference (COIN). The study has been funded by the EU, the Academy of Finland as a part of the COIN study on machine learning and advanced interfaces, and the Revolution of Knowledge Work project by Tekes.
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Learn more: EEG reveals information essential to users
Helsinki Institute for Information Technology HIIT (Finnish: Tietotekniikan tutkimuslaitos HIIT, Swedish: Forskningsinstitutet för informationsteknologi HIIT) is a joint research unit of two leading research universities in Helsinki, Finland, the University of Helsinki (UH) and Aalto University.
The work of the institute is organised in four research programmes, covering algorithmic data analysis, future Internet, network society, and probabilistic adaptive systems. Much of the work is carried out in competitively funded projects in co-operation with Finnish and international partners. The institute is led by Samuel Kaski, who was selected to continue the work of Martti Mäntylä from Helsinki University of Technology, Esko Ukkonen from University of Helsinki, and Heikki Mannila. HIIT has about 200 researchers and staff.
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Helsinki Institute for Information Technology research articles from Innovation Toronto
Researchers at the Helsinki Institute for Information Technology HIIT have developed a new search engine that outperforms current ones, and helps people to do searches more efficiently.
The SciNet search engine is different because it changes internet searches into recognition tasks, by showing keywords related to the user’s search in topic radar. People using SciNet can get relevant and diverse search results faster, especially when they do not know exactly what they are looking for or how to formulate a query to find it.
Once initially queried, SciNet displays a range of keywords and topics in a topic radar. With the help of the directions on the radar, the engine displays how these topics are related to each other. The relevance of each keyword is displayed as its distance from the centre point of the radar – those more closely related are nearer to the centre, and those less relevant are farther away. The search engine also offers alternatives that are connected with the topic, but which the user might not have thought of querying. By moving words around the topic radar, users specify what information is most useful for them.
– According to some estimate the digital universe such as data and documents is expected to grow by 2020 by a factor of 10. Tools that help us transform the time we spend in searching into discovering and understanding information will be increasingly important to enhance productivity and creativity. It is exciting to be addressing this problem in research that needs competencies from different disciplines as we uniquely combine at HIIT, states Professor Giulio Jacucci.