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

Jukka-Pekka Kauppi1, Melih Kandemir2, Veli-Matti Saarinen3

  • 1Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.

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Summary

Brain activity, specifically magnetoencephalographic (MEG) signals, can predict image relevance for future brain-controlled information retrieval systems. Combining MEG with gaze signals further enhances prediction accuracy.

Keywords:
Bayesian classificationGaussian processesGaze signalImage relevanceImplicit relevance feedbackInformation retrievalMagnetoencephalography

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Information Retrieval

Background:

  • Brain activity holds potential for controlling future information retrieval systems.
  • Predicting user relevance from neural signals is an emerging research area.

Purpose of the Study:

  • To investigate the feasibility of using brain activity to predict the relevance of visual objects.
  • To explore the combined use of magnetoencephalographic (MEG) and gaze signals for relevance prediction.

Main Methods:

  • Analysis of magnetoencephalographic (MEG) and gaze signals from nine subjects viewing image collages.
  • Decoding image relevance from MEG signals and gaze data.
  • Comparing linear and non-linear classification methods, including Gaussian process classifiers.

Main Results:

  • Image relevance was decoded from MEG signals with significantly better-than-chance performance.
  • Fusion of gaze-based and MEG-based classifiers improved prediction accuracy.
  • Non-linear classification of MEG signals outperformed linear classification.

Conclusions:

  • Brain activity, particularly MEG signals, can be effectively used to predict visual object relevance.
  • Integrating MEG and gaze signals offers a promising approach for advanced brain-computer interfaces.
  • These findings pave the way for brain-activity-based interactive information retrieval systems.