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Decoding semantic representations from functional near-infrared spectroscopy signals.

Benjamin D Zinszer1,2, Laurie Bayet2,3,4, Lauren L Emberson5

  • 1University of Rochester, Brain and Cognitive Sciences Department, Rochester, New York, United States.

Neurophotonics
|August 26, 2017
PubMed
Summary
This summary is machine-generated.

This study shows that brain activity patterns measured by functional near-infrared spectroscopy (fNIRS) can decode semantic information from words and pictures. These findings demonstrate that fNIRS data captures higher-level meaning representations across individuals.

Keywords:
functional near-infrared spectroscopymultivariate pattern analysisneural decodingsemantic model

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging

Background:

  • Understanding how the brain encodes semantic information is crucial for cognitive science.
  • Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique measuring brain activity via blood oxygenation.

Purpose of the Study:

  • To investigate whether semantic information from words and pictures is encoded in fNIRS data.
  • To determine if these semantic representations are preserved across subjects and decodable using representational similarity analysis.

Main Methods:

  • Utilized representational similarity analysis on fNIRS data collected while participants viewed audiovisual stimuli.
  • Employed leave-one-out cross-validation and compared brain activity patterns to a distributional model of semantic representation.
  • Experiment 2 involved comparing group-level models to assess decoding accuracy across different brain regions (posterior vs. lateral arrays).

Main Results:

  • Decoding accuracy for semantic information significantly exceeded chance levels in both experiments.
  • The posterior array (occipital lobe) was accurately decoded by the semantic model.
  • The lateral array (temporal lobe) showed accurate decoding in the between-groups comparison, indicating cross-subject consistency.

Conclusions:

  • Semantic representations are encoded within fNIRS data.
  • These representations are robust across different individuals.
  • fNIRS measurements can be decoded using external semantic models, linking brain activity to higher-level cognitive concepts.