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Brainprints: identifying individuals from magnetoencephalograms.

Shenghao Wu1,2, Aaditya Ramdas2,3, Leila Wehbe4,5

  • 1Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.

Communications Biology
|August 22, 2022
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Summary

Researchers developed brainprints, unique brain activity patterns, to identify individuals from Magnetoencephalography (MEG) data. This highlights privacy risks in sharing anonymized brain recordings, even across different tasks and days.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Data Science

Background:

  • Magnetoencephalography (MEG) is crucial for studying cognitive processes.
  • Open science principles encourage sharing MEG data for reproducibility.
  • Anonymized MEG data may still pose privacy risks due to individual differences.

Purpose of the Study:

  • To assess the identifiability of individuals from anonymized MEG recordings.
  • To develop interpretable features characterizing individual differences in MEG data.
  • To introduce 'brainprints' as unique identifiers of individuals.

Main Methods:

  • Proposed interpretable MEG features termed 'brainprints' to capture individual differences.
  • Validated brainprints across multiple datasets, time points, and tasks.
  • Investigated the influence of data quantity, preprocessing, and experimental tasks on identifiability.

Main Results:

  • Brainprints accurately identified individuals across different days and tasks.
  • Identifiability was maintained even between MEG and Electroencephalography (EEG) data.
  • Identified consistent brainprint components crucial for individual identification.

Conclusions:

  • Brainprints effectively characterize individual variability in MEG.
  • Sharing anonymized brain data, even MEG, carries significant privacy risks.
  • Raises concerns regarding the unregulated dissemination of brain data.