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Inter-individual single-trial classification of MEG data using M-CCA.

Leo Michalke1, Alexander M Dreyer1, Jelmer P Borst2

  • 1Applied Neurocognitive Psychology, Department of Psychology, Carl von Ossietzky University Oldenburg, Oldenburg 26129, Germany.

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Summary

This study introduces multiset canonical correlation analysis (M-CCA) to align magnetoencephalography (MEG) data across individuals. The method creates a common brain activity space, improving group analysis and enabling model transfer to new participants.

Keywords:
Brain machine interfacingDomain adaptationInter-subject alignmentMagnetoencephalographyMultiset CCASingle-trial classification

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Area of Science:

  • Neuroscience
  • Brain-Computer Interfaces
  • Signal Processing

Background:

  • Neuroscientific studies require aligning brain recordings across participants for group analysis.
  • Standard anatomical alignment in sensor space is often insufficient due to individual brain differences.
  • Magnetoencephalography (MEG) data alignment is challenging due to cortical folding and sensor variability.

Purpose of the Study:

  • To develop a method for aligning magnetoencephalography (MEG) data across participants by creating a common functional space.
  • To enable the transfer of machine learning models from a group of participants to new, unseen individuals.
  • To improve the efficiency of online brain-computer interfaces through pre-trained models.

Main Methods:

  • Multiset canonical correlation analysis (M-CCA) was employed to find a common representation of MEG activations.
  • Data from 15 participants performing a grasping task were transformed into a shared space maximizing inter-participant correlation.
  • A novel method was derived to project data from new participants into this established common space.

Main Results:

  • The M-CCA approach successfully aligned MEG data into a common functional space.
  • The method demonstrated superiority over existing approaches for inter-subject alignment.
  • The approach requires minimal labeled data from new participants for effective model transfer.

Conclusions:

  • Functionally motivated common spaces derived via M-CCA can significantly reduce training time for online brain-computer interfaces.
  • Inter-subject alignment using M-CCA facilitates data combination across participants, aiding large open dataset initiatives.
  • This technique offers a robust solution for leveraging group data in neuroscientific research and BCI development.