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Towards brain-activity-controlled information retrieval: decoding image relevance from MEG signals

We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were...

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Hauptverfasser: Kauppi, Jukka-Pekka (VerfasserIn) , Kandemir, Melih (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 13 January 2015
In: NeuroImage
Year: 2015, Jahrgang: 112, Pages: 288-298
ISSN:1095-9572
DOI:10.1016/j.neuroimage.2014.12.079
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.neuroimage.2014.12.079
Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S1053811915000026
Volltext
Verfasserangaben:Jukka-Pekka Kauppi, Melih Kandemir, Veli-Matti Saarinen, Lotta Hirvenkari, Lauri Parkkonen, Arto Klami, Riitta Hari, Samuel Kaski

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520 |a We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements. 
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